Next Article in Journal
Entity-Alignment Interaction Model Based on Chinese RoBERTa
Previous Article in Journal
A Method for Simulating the Positioning Errors of a Robot Gripper
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Advances in Solutions to Improve the Energy Performance of Agricultural Greenhouses: A Comprehensive Review

by
Rodrigues Pascoal Castro
1,2,
Pedro Dinho da Silva
1,2,* and
Luís Carlos Carvalho Pires
1,2
1
Department of Electromechanical Engineering, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
2
C-MAST—Center for Mechanical and Aerospace Science and Technologies, 6201-001 Covilhã, Portugal
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(14), 6158; https://doi.org/10.3390/app14146158
Submission received: 17 June 2024 / Revised: 8 July 2024 / Accepted: 8 July 2024 / Published: 15 July 2024

Abstract

:
The increasing global population and the challenges faced by the food production sector, including urbanization, reduction of arable land, and climatic extremes, necessitate innovative solutions for sustainable agriculture. This comprehensive review examines advancements in improving the energy performance of agricultural greenhouses, highlighting innovations in thermal and energy efficiency, particularly in heating and cooling systems. The methods include a systematic analysis of current technologies and their applications in optimizing greenhouse design and functionality. Key findings reveal significant progress in materials and techniques that enhance energy efficiency and operational sustainability. The review identifies gaps in the current knowledge, such as the need for more research on the economic viability of new materials and the development of predictive models for various climatic conditions. The conclusions emphasize the importance of integrating renewable energy technologies and advanced control systems to achieve energy-efficient and sustainable agricultural practices.

1. Introduction

Given the prediction of a global population increase to 9.8 billion by 2050 by the UN Population Division [1], and also considering the Lancet study that indicates a population peak of 9.7 billion in 2064 [2], the food production and agriculture sector faces significant challenges. These challenges are intensified by urbanization, reduction of arable land, and weather extremes due to climate change, making agricultural greenhouses vital solutions not only for food production under harsh conditions but also as platforms for the implementation of advanced technologies that seek to optimize agricultural production [3,4,5,6].
At the forefront of protected agriculture, recent scientific research has focused on optimizing the design and functionality of greenhouses, with the aim of achieving superior operational and energy efficiency. Researchers such as [7] highlight the crucial importance of innovation in greenhouse design, highlighting how the choice of materials can significantly influence energy efficiency. In addition, the integration of advanced technologies, as illustrated in the work of [4,8], demonstrates an ongoing commitment to increasing energy efficiency, contributing to the reduction in resource consumption and greenhouse gas emissions. These innovations not only increase the economic viability of agricultural operations but also respond to the urgent need for sustainable food production, aligning with the goals of environmental sustainability and long-term climate resilience.
Figure 1 highlights the complexity of the factors that affect energy efficiency in agricultural greenhouses, including the interconnection of construction and thermal elements. This comprehensive review study explores site selection to the implementation of material innovations and thermal management techniques, such as the use of air/ground exchangers, from a holistic perspective focused on sustainability. This article addresses the main factors that influence energy performance. However, it only briefly mentions Automation and Control Strategies and the use of Water Resources without providing in-depth details on these details.
Technologies such as passive solar heating, discussed by [9], and the use of geothermal energy combined with Earth-Air Heat Exchanger (EAHE) systems for thermal optimization, explored by [10,11,12], are highlighted in this study. In addition to showcasing these emerging technologies, the study aims to identify gaps in current knowledge and suggest future directions for research and development. The goal is to provide engineers, researchers, and farmers with information on efficient solutions, promoting sustainable and energy-efficient agriculture, and addressing the growing global challenges.

2. Construction Aspects for Energy Performance Improvement

The construction aspects of greenhouses encompass design and construction optimized for energy efficiency. Essential in modern agriculture, greenhouses use solar energy to maintain an ideal microclimate, favoring plant growth. The effectiveness of these structures depends on the integration of elements such as form, orientation, structure, roofing materials, and climate regulation systems. These components are designed to create perfect environmental conditions for plant physiology, reducing energy consumption and minimizing environmental impact.

2.1. Location and Orientation

Careful selection of the location and orientation of greenhouses are key to optimizing their thermal, energy, and agricultural performance. This segment addresses the importance of considering climatic and geographic variables in the choice of location and orientation, aiming to maximize sun exposure and minimize costs with heating and cooling systems. The application of advanced methodologies, such as Multi-criteria Decision Analysis (MCDA) and Geographic Information Systems (GIS), assists in identifying the most suitable sites, emphasizing the need for an integrated and informed approach to building efficient greenhouses.

2.1.1. Choosing the Location and Orientation of Greenhouses

The effectiveness of greenhouses, both in terms of agricultural productivity and thermal and energy efficiency, is highly influenced by their location and orientation. The proper choice of these factors is essential to optimize sunlight harvesting and offer protection against wind, directly impacting the thermal balance of the structure [13,14].
Climatic factors such as temperature, humidity and solar radiation are determinants in the energy operation of greenhouses. A strategic location, taking these factors into account, can significantly reduce the need for additional heating and cooling systems [15,16].
When choosing the ideal location for greenhouses, several technologies offer significant benefits, but they also have limitations. Geographic Information Systems (GIS) perform detailed spatial analysis when collecting, storing, processing, and visualizing geographic data. This process involves integrating information on topography, water resources, and other relevant environmental factors from a variety of sources. Using mapping and spatial analysis tools, GIS allows users to examine the physical characteristics of an area and assess potential locations for greenhouses, thereby identifying the most suitable locations. However, the effectiveness of this process may be affected by the availability and timeliness of geographic data, limiting its accuracy and applicability [17,18,19].
Multicriteria Decision Analysis (MCDA) is a process that involves defining relevant criteria (e.g., cost, environmental impact) and assigning weights to those criteria to reflect their relative importance. The location options are then evaluated and compared based on these weighted criteria. Using mathematical and statistical methods, MCDA makes it easy to identify the most balanced or advantageous option, allowing for informed decision-making that considers multiple aspects simultaneously. However, the selection of criteria and the assignment of weights can introduce an element of subjectivity into the process, affecting the objectivity of the analysis [20].
Digital Elevation Data Modeling (DEM) is performed by collecting and analyzing data on the terrain elevations of a specific area, creating a three-dimensional (3D) model of the earth’s surface. This process involves the use of remote sensing techniques, such as satellites or aerial surveys, to measure the height of the terrain at various points, which are then interpolated to generate a continuous representation of the topography. The resulting model allows for detailed topography analyses, which are key to determining the optimal orientation and sun exposure for a greenhouse. However, the accuracy of the DEM depends on the spatial resolution of the data collected, which may not be sufficient to identify minor topographic variations affecting the microclimate within the greenhouse [18].
The process of Remote Sensing and the use of Satellite Imagery involves the capture of images of the Earth’s surface by satellites equipped with remote sensors. These sensors collect data in various ranges of the electromagnetic spectrum, which are then processed to generate images that reveal information about land use, vegetation, water resources, and other environmental conditions. The analysis of these images allows you to monitor changes, identify trends, and evaluate natural resources, providing a basis for environmental planning and management. However, the usefulness of this information for detailed analysis may be limited by the spatial resolution of the images and the frequency with which they are updated, which may not capture fine details or rapid changes in the environment [21,22].
Climate Analysis is performed by collecting historical and current weather data, such as temperature, precipitation, humidity, and wind speed, and applying these data to climate models to predict environmental conditions in a specific area. These models consider the interaction between various atmospheric and geographic factors to simulate future climate. While climate models offer valuable insight into overall climate trends and aid in greenhouse location decision-making, they can struggle to capture site-specific microclimate variations, which are crucial for accurate agricultural planning [23,24].
To choose the location of a greenhouse in a simplified way, sun exposure, the topography of the land, access to water, and soil quality are evaluated. It is also important to consider ease of access for transportation, check local regulations, and estimate construction and operating costs. Expert consultations can offer additional insights to avoid common mistakes, thus balancing practical and economic factors without the need for complex analyses. This method may not capture important microclimatic variations and underestimate the complexity of crop-specific needs. The relevance of these factors is supported by studies such as Esmaeli and Roshandel [25], which emphasize the importance of optimized solar design for greenhouses based on specific climatic conditions, and Mohammadi et al. [26], which experimentally validate dynamic models to predict indoor environmental variables in semi-solar greenhouses. These studies provide a solid scientific basis for simplified greenhouse site selection criteria, highlighting the intersection between sustainable agricultural practices and energy efficiency.
The orientation of the greenhouse in relation to the sun is crucial to maximize sun exposure, which is essential for photosynthesis. In the Northern Hemisphere, the ideal orientation is to the south, while in the Southern Hemisphere, it is to the north, aiming to capture the maximum amount of sunlight in the cold months [27,28], (see Figure 2 and Figure 3). In addition, proper guidance can improve natural ventilation and prevent overheating, contributing to an optimal indoor microclimate, which is vital for optimal crop development [28,29].
Comparative studies highlight that an east–west orientation can offer significant energy savings, highlighting the importance of careful planning of greenhouse orientation [30,31]. This strategic orientation not only optimizes sunlight harvesting but also photosynthetic efficiency and thermal control, which are fundamental for the success of agricultural production in controlled environments.
The use of Multi-criteria Decision Analysis (MCDA) and Geographic Information Systems (GIS) has proven to be effective in selecting optimal locations and orientation for greenhouses, considering environmental, geophysical, and socioeconomic criteria [32,33]. In many areas of research, the Fuzzy-DEcision-MAking Trial and Evaluation Laboratory (F-DEMATEL) methodology has been used, because it allows to complement these analyses by optimizing the selection of sites for energy efficiency [34]. The F-DEMATEL methodology is an advanced approach within Multicriteria Decision Analysis (MCDA) that uses fuzzy logic to address uncertainties and subjectivities in the evaluation of criteria and their interactions, when choosing the location and orientation of renewable energy facilities, infrastructure projects, and urban planning, considering the environmental, social, and economic impacts of such decisions.
This technique allows a detailed analysis of the interdependencies between variables such as water resources, soil quality, and sun exposure, improving decision-making under uncertain conditions. However, its effectiveness depends on the quality of the input information and the correct application of fuzzy logic concepts, posing challenges such as potential subjectivity in evaluations and the need for specialized knowledge for their implementation. Despite these limitations, F-DEMATEL offers a powerful tool for the systematic and enhanced analysis of the complexities involved in site selection and guidance for sustainable development projects, critical infrastructure, and implementation of renewable energy technologies, promoting more informed and robust decisions [35,36]. After research, no record of direct use of the F-DEMATEL methodology was found in the selection of sites and guidelines for greenhouses, which indicates an opportunity for future research to explore its applicability in this specific context.
Therefore, the location and orientation of the greenhouses are decisive for their thermal and energy efficiency and productivity. The careful choice of these aspects, combined with the use of advanced geographic analysis and climate modeling technologies, can lead to an optimized performance of the greenhouses, maximizing the sustainability and profitability of the agricultural project. The orientation of the greenhouse in relation to the sun, the use of natural shade and the adaptation to local climatic characteristics are determinants for the efficiency of solar energy harvesting [37].
Figure 2 and Figure 3 illustrate comparative studies of the daily solar incidence in greenhouses with two distinct orientations: east–west and north–south. In both figures, the greenhouses are represented by even-span models, and the sun’s trajectory is symbolized by solar icons positioned at key times of the day: sunrise, noon, and sunset.
Several recent studies on greenhouses highlight the importance of orientation, shape, and cover in energy efficiency and productivity. Stanciu et al. [30] compared two different orientations of a vegetable greenhouse with respect to the east–west axis in Romania. The solar irradiation received by the greenhouse is estimated and the indoor air temperature profiles are numerically simulated throughout the day, in the winter and summer periods. The heating and cooling loads required to maintain a constant indoor air temperature during vegetation are calculated. The simulations reveal energy savings for east–west versus North–south orientation in both summer and winter. This study provides valuable insights for the design of heating and cooling equipment, ensuring the optimal microclimate within the greenhouse.
Aissa and Bezari [31] looks at how solar radiation intensity and wind direction affect the internal temperature of tunnel greenhouses. It was observed that airflow patterns (laminar, transitional, and turbulent) influence the temperature behavior, also related to the variation of the drag coefficient as the Reynolds number changes. Using simulations in the Fluent software, the study integrates aerodynamic and thermal phenomena to understand the temperature variations inside the greenhouse as a function of wind direction and speed, concluding that these factors have a significant impact on the internal temperature of the greenhouse.
Cossu et al. [16] analyzed the climatic conditions inside a greenhouse with south-facing photovoltaic roofs, where 50% of the roof area was replaced by photovoltaic (PV) modules, resulting in a 64% reduction in the availability of indoor solar radiation compared to a greenhouse without a PV system. This change in the availability of sunlight affected the productivity of crops, specifically tomatoes, even with the use of supplemental lighting powered by the energy generated by the PV system. The study highlighted the importance of considering the impact of shade caused by PV modules on crop yields when integrating photovoltaic systems into agricultural greenhouses.
Odesola and Ezekwem [38] provided a comprehensive review on shape selection and orientation for greenhouses, emphasizing the importance of these factors in energy efficiency and plant productivity. This article highlights that the proper choice of greenhouse shape and orientation can maximize solar radiation uptake, thereby optimizing the indoor environment for plant growth, as well as reducing the need for additional energy for heating or cooling. The detailed analysis offered by the study helps to understand how these design decisions directly impact the sustainability and operational effectiveness of greenhouses in different climates.
Moreover, Gupta et al. [39] used a three-dimensional shadow analysis in Auto-CAD to calculate the total solar fraction in greenhouses. The research highlighted that greenhouse orientation is crucial for the distribution of solar radiation and for energy efficiency. The study evaluated how different orientations of the greenhouse affect the amount of solar radiation transmitted to the interior, considering the influence of orientation on the loss and gain of solar radiation during the seasons. The 3D shadow analysis in Auto-CAD allowed an accurate assessment of the total solar fraction, underscoring the importance of correct orientation to maximize solar capture and improve the energy performance of greenhouses.
Studies such as Mercan and SEZG˙IN [32] have used Multicriteria Decision Analysis (MCDA) and Geographic Information System (GIS) to identify optimal locations in Aydin, Turkey. Five main criteria (topography, soil, climate, water, and economy) and two constraints (land-use capacity and proximity to surface water resources) were considered, based on literature and expert opinions. A total of 160 sites were chosen in existing greenhouse areas through intentional sampling, questioning their suitability. The Analytical Hierarchy Process (AHP) method was used to weight the criteria, revealing that 2.4% of the area was “most adequate”, 33.4% “adequate”, 31.4% “moderately adequate”, 0.7% “slightly adequate”, 29.6% “inadequate”, and 2.5% “outside the evaluation”. Of the sample sites, 1.9% were considered “most adequate”, 10.1% “adequate”, 0.6% “moderately adequate”, and 87.3% “inadequate”. It was concluded that most of the land was not suitable for greenhouses, mainly due to the distance from water resources and the classification of land use. Recommendations have been made for more effective site selection for greenhouses and other agricultural production areas.
Vanthoor et al. [33] highlighted the importance of adapting greenhouse design to local climatic conditions, emphasizing the need for flexible design tools, and concluded that optimizing greenhouse design for the local climate is a key factor in maximizing agricultural productivity.
Ahamed et al. [40] conducted a detailed study on energy conservation strategies to minimize heating costs in greenhouses, addressing solutions such as energy-efficient optimized designs, special roofs, thermal curtains, and advanced heating systems. They emphasized the need to balance energy savings with plant growth requirements, aiming for sustainability and energy efficiency in colder climates.
Saltuk and Artun [41] also used GIS and MCDA for the selection of greenhouse sites in Turkey, considering variables such as topography and proximity to geothermal resources. The study compares these four provinces with Antalya, a leading greenhouse production area in Turkey, by analyzing factors such as climate, soil, and other geographic criteria through a multicriteria assessment using Geographic Information Systems (GIS). The objective is to determine the feasibility of these provinces for the development of greenhouses by identifying suitable, unsuitable, or partially suitable areas. The results, validated by field studies and comparisons with conditions in Antalya, concluded that geothermal heating can be an effective energy solution, although it needs a deeper assessment of environmental impacts.
Hernandez et al. [42] developed models based on Bayesian networks to predict energy demand in high-tech greenhouses, demonstrating that these models, by capturing the interrelationships between multiple variables, can anticipate energy needs with great accuracy. These models are able to identify and understand the interrelationships between various variables within the greenhouse environment, which allows them to make accurate predictions about energy needs. The study validates the effectiveness of these models in predicting energy demand, concluding that they are valuable tools for the development and improvement of energy control systems, contributing significantly to energy efficiency in advanced greenhouses.
Based on the extensive investigations carried out, it is possible to infer that the optimal location and orientation of a greenhouse is multifaceted and dependent on a complex combination of geographical and climatic factors. Table 1 summarizes the significant contributions of several studies on the influence of greenhouse location and orientation on energy efficiency, crop yield, and sustainability. It is presented to provide a quick reference and comparison of the results and conclusions of the aforementioned studies.

2.1.2. Influence of Solar Radiation on Greenhouses

Solar radiation, the primary source of energy for greenhouses, is essential in the cultivation of plants and the energy efficiency of agricultural operations. The design of modern greenhouses seeks to maximize sunlight absorption by adjusting specifically to the demands of the plants and the climatic conditions of the site.
The intensity and quality of sunlight are modulated as needed through shading systems and the choice of appropriate roofing materials because they are critical to maintaining an optimal growing environment, as discussed by [46,47]. Such decisions are guided by factors such as latitude, altitude, seasonality, and meteorology, with the aim of maintaining an optimal environment for vegetative development [48,49].
Huang et al. [50] proposes an improved model to calculate the total solar radiation transmitted by transparent curved surfaces in greenhouses. This model emphasizes the importance of external soil reflection, a component often overlooked in previous studies, highlighting how solar radiation reflected by the soil contributes significantly to the total radiation received inside the greenhouse. By incorporating external soil reflection, the model offers a more accurate and comprehensive approach to the efficient design of solar greenhouses, allowing for the optimization of the greenhouse’s aspect ratio for different latitudes in order to maximize solar radiation capture and improve the plant growth environment.
The insertion of photovoltaic technology in greenhouses provides dual functionality: Converting solar energy into electricity to power indoor operations, decreasing reliance on external energy sources, and providing power for irrigation systems and other agricultural equipment [44,51,52]. This approach not only lowers operating costs but also reinforces the facility’s energy self-sufficiency.
The integration of photovoltaic systems in greenhouses, such as organic semi-transparent solar cells (OSCs), can create an annual energy surplus in hot and moderate climates while minimizing the reduction of sunlight entering the greenhouse with an appropriate design [44]. Zhang et al. [53], developed mathematical models to optimize the distribution of sunlight within Chinese solar greenhouses, highlighting the importance of understanding the impact of solar radiation on the greenhouse environment. These models are critical not only to assess the quality of the light environment, but also to enable the optimization of greenhouse design, lighting regulation, and planting pattern, with a view to maximizing energy efficiency and improving crop growing conditions. By quantifying the allocation of solar radiation and obtaining the dynamic spatial distribution of solar radiation, the study provides valuable information to enhance the low-light environment near the north wall and optimize the storage capacity and heat release of the north wall, which are critical aspects for the success of solar greenhouse farming.
The integration of photovoltaic (PV) systems in greenhouses faces several limitations, including high implementation and maintenance costs that may hinder their large-scale adoption [54]. Technical challenges such as the limited efficiency of solar cells due to losses from radioactive and non-radioactive recombination, parasite resistance, and poor light management are also significant barriers [55]. Shading caused by nearby structures or by the greenhouse structure itself can reduce the power generated by the PV system, severely affecting its performance [56]. In addition, the connection of photovoltaic plants to the electrical system requires careful analysis to ensure voltage stability, which poses another technical challenge [57]. These limitations highlight the need for ongoing research and development to overcome the technical and economic hurdles associated with implementing greenhouse PV systems.
The use of linear Fresnel lenses in a greenhouse as a concentrated photovoltaic system allows direct solar radiation to be retained while diffuse radiation passes through and enters the greenhouse’s growing system, improving energy efficiency and reducing the need for cooling in the summer [58]. The study by Wu et al. [59] introduces a photovoltaic/thermal concentrator (CPV/T) system with a cylindrical Fresnel lens in a Chinese solar greenhouse. The system uses a Fresnel lens to concentrate light without blocking active radiation into the photosynthesis needed by plants. This demonstrates that the system can significantly improve the utilization rate of non-planting space and increase the comprehensive utilization efficiency of the solar power system without negatively affecting plant growth areas.
Li et al. [60] have developed a solar heating system for greenhouses, which operates with a Fresnel lens concentrator and underground heat storage. This system provides continuous heating to the greenhouse during periods without sunshine, promoting a safe environment for plant growth. The research details the construction and functionality of the system, showing through experiments that by burying the heating pipe at a depth of 1.65 m, heat dissipation in the soil occurs in about 5 days, increasing the soil temperature by approximately 4 °C. This mechanism ensures that the temperature inside the greenhouse remains above 8 °C, even under adverse weather conditions, without the need for additional energy sources, thus meeting the essential temperature requirements for crop development.
The integration of Fresnel lenses into greenhouses can face several limitations, including challenges in uniform light distribution, which can affect plant growth due to the specific design of lenses that focus light on concentrated areas. In addition, aspects such as high installation and maintenance costs, the need for specialized technical knowledge, environmental concerns regarding the life cycle of the materials used, and the susceptibility to external physical conditions such as obstructions and climatic variations, can compromise the efficiency and viability of such systems. However, technological advances and innovative design solutions can help overcome these limitations, enhancing the use of Fresnel lenses for sunlight optimization in agricultural applications [59,61,62,63].
Solar radiation, when effectively managed, can significantly improve crop production and greenhouse energy efficiency [44]. The theme of solar radiation in greenhouses is widely studied by researchers from various areas, such as Agricultural Engineering, Physics, Chemistry and Biology. In addition to the works already referenced, we can also highlight the contributions of (Gorjian et al. [64]) who reviewed the opportunities for implementing solar energy technologies in agricultural greenhouses, including the integration of photovoltaic and thermal collectors. The study describes that the use of thermal energy storage (TES) systems can increase the overall thermal performance of solar greenhouses by 29%. They emphasized the need for continuous improvements in technology, as well as the importance of TES units in making solar greenhouses a sustainable solution.
Ghani et al. [65] discussed the challenges of greenhouse design in hot and arid environments, considering cooling strategies and sustainable technologies. They examined important design features of the greenhouses, such as dimensions, orientation, shapes, roofing materials, and shading, as well as effective cooling methodologies and operational strategies for maintaining suitable climatic conditions. In this article, they also highlighted the use of sustainable technologies and clean energy applications to minimize dependence on fossil fuels and promote efficient water management.
Mazzeo et al. [66] analyzed the performance of solar greenhouses in different climates, highlighting the importance of choosing roofing materials and proper ventilation. The study used simulations considering radiative exchanges, airflow, evapotranspiration and heat transfer. More effective glass solutions have been identified, and these work better in winter than in summer. The research highlighted the importance of choosing roofing materials and proper ventilation to prevent indoor overheating in solar greenhouses.
To contextualize the importance of solar radiation in the optimization of the greenhouse environment, Table 2 summarizes the selected studies that address various aspects of the optimization of solar radiation for energy efficiency in Greenhouses.

2.2. Greenhouse Construction

The construction of agricultural greenhouses incorporates a set of ingenious practices aimed at optimizing the interface between advanced building materials and innovative design strategies, with the goal of maximizing energy efficiency and promoting sustainable plant growth. This approach is based on the careful selection of materials with insulating properties and resistance to extreme environmental variables, aligning with sustainability and energy efficiency guidelines. The architectural design of the greenhouses is meticulously planned to make the most of the available solar radiation, which is essential for photosynthesis while minimizing energy needs for heating, additional lighting, and ventilation.

2.2.1. Greenhouse Shapes and Their Impacts

The shape of the greenhouse is the external geometric and structural configuration that directly influences energy efficiency, light harvesting and internal ventilation [67]. This shape is designed not only to withstand local weather conditions such as snow and strong winds but also to optimize sunlight capture [7,65]. In summer, the goal is to minimize sun exposure to prevent overheating, while in winter, the goal is to maximize exposure to take advantage of natural warmth [68,69].
In Figure 3, we observe the diversity of forms for agricultural greenhouses, each with unique characteristics. The Flat Roof Greenhouse is notable for its simple design, ideal for areas with less snow, but requires care with drainage and ventilation, see Figure 4d. The Quonset Greenhouses (Figure 4e), with their arched roof, are effective at dispersing winds and snow runoff, as well as maximizing sunlight capture, offering advantages such as wind resistance and modularity [7,69]. Geodesic greenhouses or dome roofs (Figure 4c) stand out for their even distribution of sunlight and robustness against winds and snow, suitable for tall and energy-efficient plants, although they can be more expensive to build [70]. The Greenhouse in Serra or Chapel (Gable) (Figure 4b), combines functionality and aesthetics, optimizing sun exposure and providing space for taller plants, but with variable construction costs [71]. Modified arch greenhouses (Figure 4a), characterized by their elongated shape, are economical and versatile, perfect for seasonal crops and protection against weather extremes [69,72]. Finally, the Sierra Roof Greenhouses offer excellent ventilation and maximize exposure to sunlight, making them ideal for large-scale agricultural productions, despite a higher initial investment [7,28,38,69,72]. Each type of greenhouse caters to different needs and preferences in terms of climate, cultivation, and budget.
Greenhouses, if built in span shapes, are also classified as even-span, Uneven-span or multi-span [7,69,72,73]. The even-span greenhouses have two equal sloping sides, forming a symmetrical roof (similar to the chapel-type greenhouse Figure 4b), and have a continuous and wide space, ideal for uniform productions and automation. Uneven-span greenhouses are similar to regular-span greenhouses, but with sloping sides of different heights and are more commonly used on sloping terrain or to adapt to specific sun exposure requirements. Multi-span greenhouses, on the other hand, are a set of greenhouses that have independent sections, allowing different micro-climates for various crops, as we can see in Figure 4f, Refs. [74,75]. It is worth noting that the classification of greenhouses may vary depending on the source consulted and the geographic region.
Table 3 presents a comprehensive overview of the advantages, disadvantages, and recommended climate of greenhouse forms, from the simple constructed flat greenhouses to the more complex saw-shaped roof greenhouses. The decision on the form of the greenhouse to be used depends on the cultivation objectives and local conditions, as well as the specific needs of each agricultural production. In addition, Table 4 provides a summary of the different forms of greenhouses and their respective indications. These tables provide information for farmers and designers who want to choose the most suitable greenhouse shape for their specific needs, considering the advantages, disadvantages, and indications of each greenhouse shape.
There are many researchers who are dedicated to studying different forms of greenhouses and how they can be optimized to improve the use of solar energy and food production. It is a growing area of research, especially with the increased interest in sustainable solutions for food production and the use of renewable energy.
Choab et al. [85] applied dynamic simulations to conclude that certain orientations and structures, such as Quonset, improve energy efficiency in Morocco. The study concluded that in Agadir, Morocco, the east–west orientation can reduce air-conditioning costs by 9.28%, while the Quonset-shaped structure provides an even greater saving of 14.44% in annual HVAC costs compared to regular airhouses. In addition, the use of Polycarbonate Hollow Sheets (PHS) as the roofing material reduces the heating demand by 29.2%, and the use of air as a filler gas in Polyethylene (PE) roofing is economically advantageous.
Karambasti et al. [86] used genetic algorithms to find that Quonset greenhouses in Iran capture solar energy better. The research focuses on optimizing greenhouses in northern Iran to maximize solar energy use by analyzing three common types of greenhouses (Span-Regular, Modified Arc, and Quonset) and three area sizes for each type. A mathematical model calculates the radiation loss and the available solar radiation, aiming at multi-objective optimization for higher solar capture in winter and lower in summer. For regular-span greenhouses, the ideal wall height is 3.8 m with a roof angle of 16°. In modified arc greenhouses, the ellipse ratio varies, 0.8 for small and large sizes, and 0.25 for medium. For Quonset, the ratio is 0.6 for medium and large, and 0.8 for small. The Quonset type is the most efficient for annual solar use.
Marucci et al. [81] conducted an evaluation using a prototype photovoltaic greenhouse. In this study, we analyze the evolution of PV installation in greenhouses and the effects of shading on crops under these structures. Specifically, it examines tunnel greenhouses, whose curved shape challenges PV installation. Using transparent flexible PV panels of 1116 m × 0.165 m, with an efficiency of 18%, the study evaluates the shading variation within a prototype greenhouse. It was observed that the shading, caused by the checkerboard arrangement of the panels, does not exceed 40% throughout the year, with significant seasonal changes.
In the study of Sahdev et al. [69] evaluated the influence of the shape and orientation of regular-span greenhouses on their thermal efficiency, comparing five common configurations (even-span, uneven-span, vinery, modified arch and quonset) in relation to solar radiation absorption at different latitudes and orientations (east–west and north–south).
Through mathematical modeling and experimental validation using data from a typical summer day in Ludhiana, India, the results indicated that the east-to-west uneven-span shaped greenhouse maximizes solar radiation reception during winter while minimizing it in summer, which is preferential for year-round crop production at various latitudes. This standard suggests that careful selection of greenhouse shape and orientation can lead to significant improvements in indoor thermal management, with indoor air temperature variations that can be as high as 4.6 °C, depending on the configuration of the greenhouse.
The research by Mellalou et al. [72] compares the performance of three common forms of greenhouses (even-span, uneven-span, and elliptical) under the climatic conditions of Marrakech, Morocco, while keeping the dimensions (length, width, and height) of the greenhouses the same. A mathematical model was developed to calculate the total solar radiation per hour received by each form of greenhouse throughout the year, for both north–south and east–west orientations. The study concludes that the uneven-span greenhouse with a roof slope of 12° is the most optimal, followed by the even-span with a slope of 17° and, lastly, the elliptical. Therefore, for Marrakech, the uneven-span greenhouse with a 12° inclination and east–west orientation is preferable.
In the study conducted by Sahdev et al. [7] greenhouse technology is explored as a practical option for the production and drying of agricultural products in controlled environments. The importance of the proper selection of shapes and orientations of greenhouses for the success of the design is highlighted. The most common forms of greenhouses are the regular-span roof and the Quonset form, mainly used for growing plants and drying. Various forms of greenhouses and their applications are examined in detail in the study.
Table 5 presents an overview of the studies carried out on the theme of greenhouse forms, highlighting the authors, years of publication, titles and abstracts of these studies.

2.2.2. Covering and Insulation Material for Greenhouses

Greenhouse covers refer to the materials used to cover and protect the structure of the greenhouse [89]. Proper selection of roofing materials and insulation strategies is crucial for optimizing the indoor microclimate, maximizing energy efficiency, and promoting optimal plant growth. The choice of greenhouse cover also significantly impacts the quality and quantity of light entering the greenhouse, which is essential for photosynthesis [90,91]. For example, some coatings can filter or block certain wavelengths of light, which can affect photosynthesis and other biological processes [89].
Greenhouse cover plays a crucial role in the efficient transmission of sunlight, a process measured by transmittance. This essential parameter indicates the percentage of sunlight that can pass through the roofing material [90,92]. At the same time, proper thermal insulation is essential to maintain the heat needed for crop development. As solar radiation reaches the surface of the greenhouse cover material, some fractions of this radiation are absorbed, others are reflected, and the remaining portion is transmitted through the cover into the greenhouse, as illustrated in Figure 5. The selection of materials with optimal transmittance properties allows the entry of light necessary for plant growth, while minimizing heat loss, contributing to energy efficiency [93].
There are several types of roofing materials available for greenhouses, each with its own profiles for transmittance, thermal insulation, durability, and cost. Glass, for example, offers high transparency and longevity. Low-E glass and integrated photovoltaic glass are innovations that maintain the transparency necessary for photosynthesis while generating energy. Polymeric plastics, such as polyethylene (PE), polycarbonate (PC), and ethylvinylacetate (EVA), are valued for their lightness and cost-effectiveness. The durability and UV resistance of these materials have been improved with additives and treatments [90,94,95].
Thermal insulation is vital for reducing heat loss and minimizing heating costs. Materials such as multi-wall polycarbonate and polyethylene films with air bubbles or insulating foams can be used to improve thermal performance [40]. Rock wool, polystyrene, and insulating foams are commonly used to improve heat retention, which is essential in cold climate regions [40,96,97].
Greenhouse covers should have high levels of transparency to visible light and low permeability to infrared radiation. It is important that they maintain their clarity and do not suffer significant reductions in light transmission over time, thus ensuring an extended lifespan [98,99]. In addition, they must offer robustness against weather conditions, such as gusty winds, to ensure durability and protection of crops [29,100,101].
Each greenhouse roofing material has specific limitations that influence its selection: polyethylene plastic, although economical, has limited durability and poor thermal insulation, requiring frequent replacement; polycarbonate offers better insulation and strength but with a higher cost and susceptibility to scratches that can affect light transmission; glass provides excellent transparency and durability, but it is expensive, heavy, and fragile, requiring robust support structures; air-inflated double-layer polyethylene films improve thermal insulation, but require continuous maintenance of the inflation system and are vulnerable to punctures and light-diffusing films distribute light evenly, but are more expensive and can suffer UV degradation. Choosing the ideal material depends on a balance between cost, durability, plant needs, and local climatic conditions, requiring careful evaluation of pros and cons [95,102,103].
Advanced materials, such as aerogels and nano-structured membranes, are being researched for their superior insulating properties and ability to selectively filter light spectra, optimizing transmittance for plant-specific needs [104,105]. Light-diffusing covering material can promote more uniform plant growth, avoiding spots of intense light that can cause stress or burns [106].
The graph in Figure 6 is a visual representation of the transmissivity of light through different materials used in greenhouse roofs [7]. Transmissivity is expressed as a percentage, indicating the fraction of incident radiation that is transmitted through the material. With 86% transmissivity, the glass is highly transparent, allowing a large portion of sunlight to pass through, which is beneficial for most greenhouse applications. With a transmissivity of 81%, slightly lower than glass, Acrylic is known for its strength and clarity, although it filters a little more light. With 77% transmissivity, polycarbonate allows less light to pass through compared to glass and acrylic but offers advantages such as durability and impact resistance. Glass fiber-reinforced plastic panels have 82% transmissivity, a middle ground between polycarbonate and acrylic, combining transparency and strength. With 90% transmissivity, polyethylene is a highly translucent material, making it an economical and efficient option for light transmission in greenhouses. The choice of roofing material for a greenhouse depends on a balance between the amount of light desired to optimize plant growth and other material properties such as weather resistance, durability, and cost.
The review article of Maraveas et al. [90], discusses the development and use of nano and microscopic materials to improve thermal insulation in greenhouses while maintaining visible light transmission crucial for photosynthesis. These advancements aim to minimize heat loss while generating significant energy savings. The analysis includes materials such as glass, plastic films and sheets, as well as additives such as fillers and dyes. Graphene, fullerene and phase transition materials (PCMs) are noteworthy for their potential to insulate against radiation, suggesting their applicability in greenhouse covers. The review categorizes advances in semi-transparent photovoltaic materials and zinc oxide-based films, emphasizing their thermal insulation and light transmission properties. The development of new roofing materials for greenhouses is anticipated, despite climate concerns. The study concludes by highlighting the positive impact of these technologies on sustainability, energy efficiency, reduction of C O 2 and agricultural productivity, also highlighting the economic benefits, such as the reduction of operating costs through the use of PCMs to stabilize temperatures.
Maraveas [95] looked at the sustainability of greenhouse roofing materials, highlighting the adaptable properties of polymers and the superior optical characteristics of glass. It concludes that polymers offer personalization and recycling, while glass better transmits the light needed for photosynthesis, both of which are key to long-term sustainable adoption.
In the work of Li et al. [107], a new internal insulation method was proposed for Chinese greenhouses, addressing the 60% heat loss through the roof. Through mathematical modeling and energy balance, the method resulted in a 13.67% reduction in heat loss from the roof and an improvement of 2.01 K in internal temperature, enhancing the thermal efficiency of the greenhouses.
Mavroeidis et al. [108] evaluated the impact of different greenhouse covering materials on the performance and yield of hemp, using five treatments with different polyethylene covering films. The results show significant variations in agronomic traits and hemp yields, with the G1 treatment showing the best results: increased soil temperature and PAR values between 11 and 16% and 50 and 110%, respectively, compared to the control G4. This resulted in an improvement in most bud characteristics including weight, compression index and CBD content, with increases of up to 75% in CBD yield per plant compared to G4.
The findings reinforce the importance of considering the optical properties of greenhouse covering materials, suggesting that the appropriate choice can significantly improve hemp agronomic traits and increase CBD yields by up to 75%.
Papadakis et al. [109] reviewed the radiometric and thermal properties of greenhouse covering materials, focusing on transmittance, reflectance and absorbance. The study highlighted inadequacies in testing methods, suggesting the need to develop new specific methodologies for these materials. Table 6, summarizes some of the recent work on covering/insulation in agricultural greenhouses, highlighting their main findings and contributions to understanding and optimizing the use of greenhouses in different contexts and climates.

3. Thermal Aspects for Improving Energy Performance

As illustrated in Figure 1 on the Integrative Model of a greenhouse, thermal aspects are crucial in the efficient design and operation of these structures. They are responsible for maintaining ideal climatic conditions, including adequate temperature, controlled humidity, adjusted C O 2 levels and optimized lighting to promote photosynthesis. These measures not only boost agricultural productivity but also contribute to environmental sustainability and resource savings, in line with new technologies and control strategies integrated into protected agriculture.

3.1. Maintenance of the Greenhouse Environment

Maintaining the environment in greenhouses is a crucial aspect of ensuring optimal plant growth and agricultural production efficiency. In a greenhouse, it is possible to control several environmental factors, such as temperature, humidity, light and C O 2 . These four factors are interdependent and each plays a vital role in optimizing the health and productivity of plants grown in controlled environments. Proper management of these elements makes it possible to create an environment favorable to crop development.

3.1.1. Temperature Maintenance

Temperature regulation in greenhouses is a fundamental element in agronomy and plant science, playing a crucial role in controlling physiological processes essential for plant growth and development. Adequate temperature is vital to optimize photosynthesis, a fundamental biological process where plants convert Carbon Dioxide ( C O 2 ), Water ( H 2 O ) and Light into Oxygen ( O 2 ) and Glucose ( C 6 H 12 O 6 ), as represented by the Equation (1). This process occurs mainly in leaves, where chlorophyll and other photosynthetic pigments capture light energy [112].
6 C O 2 + 12 H 2 O + LUZ C 6 H 12 O 6 + 6 O 2 + 6 H 2 O .
Specific temperature ranges promote maximum photosynthetic efficiency, while temperatures outside these ranges can limit the ability of plants to carry out this process effectively, thus affecting the production of their own energy and food [112,113,114].
In addition to photosynthesis, temperature directly influences the rate of transpiration, essential for thermal regulation, nutrient absorption and maintenance of cell turgor. Optimal temperatures ensure a balanced transpiration rate, crucial to the plant’s overall health. However, temperature extremes can lead to imbalances, resulting in water stress or reduced photosynthetic activity, and, consequently, negatively impacting plant growth and productivity [115].
In this way, the integration of strategies for effective temperature regulation in greenhouses emerges as an essential agronomic practice, aimed at maximizing the efficiency of photosynthesis and ensuring an optimal transpiration rate, contributing to sustainability and increased agricultural production.
Temperature also influences other aspects of plant development, such as seed germination, root growth, flowering and fruit ripening. Each phase of the plant’s life cycle may have specific temperature requirements to optimize development [116].
To maintain temperatures within ideal ranges, greenhouses employ heating and cooling systems, such as gas, electric, or oil heaters, and water misting or natural ventilation systems. The implementation of automated control technology allows continuous temperature adjustments, ensuring optimal conditions for production [117].
Effective temperature management in greenhouses is, therefore, a critical scientific practice that integrates knowledge in plant physiology, engineering and monitoring technology. This multidisciplinary approach is essential to maximize the efficiency of agricultural production, contributing to global food security in the context of climate change and increasing demand for water resources.

3.1.2. Humidity Maintenance

Humidity regulation in greenhouse environments is a topic of academic and practical relevance in agronomy and plant science, acting as a determining factor in optimizing the microclimate for plant development. According to scientific literature, relative air humidity directly influences the transpiration rate of plants and the ability of leaves to absorb C O 2 during the photosynthesis process, two fundamental mechanisms for biomass production and photosynthetic efficiency [118,119].
Transpiration, as an essential physiological process, helps in the plant’s thermal regulation, nutrient absorption and maintenance of cell turgor. However, excessively high relative humidity can decrease the vapor pressure gradient between the leaf and the environment, reducing the rate of transpiration and, consequently, limiting the absorption of C O 2 and the translocation of nutrients. This can result in stunted vegetative growth and increased susceptibility to pathogens, due to air stagnation and increased leaf moisture, which favor the proliferation of fungi and bacteria [116,120,121].
On the other hand, low relative humidity can accelerate water loss through transpiration, exceeding the water absorption capacity of the roots, leading to a water deficit that results in water stress, stomatal closure and ultimately, a reduction in photosynthesis. Prolonged water stress can cause irreversible damage to cellular structure, severely limiting plant growth and productivity [117].
Thus, humidity management in greenhouses emerges as a critical component in agricultural management, requiring a multidisciplinary approach that integrates knowledge of plant physiology, meteorology and engineering. The use of remote sensing technologies for continuous monitoring of relative humidity, together with automated climate control systems, represents a significant advance in the precision of environmental management in greenhouses, enabling real-time adjustments to maintain humidity within optimal ranges for each species.
To effectively manage humidity in greenhouses, various equipment and strategies are used to balance the relative humidity of the air. Ventilation systems, for example, are essential for renewing air and eliminating excess humidity, while misting or sprinkler systems can increase air humidity through the evaporation of finely sprayed water [122]. Irrigation control is another crucial aspect, adjusting the amount of water supplied to plants according to specific water needs and avoiding excess moisture in the soil. Ground covers and absorbent materials are used to keep soil moisture regulated, reducing unnecessary evaporation and conserving water.
Environmental maintenance technologies in greenhouses are essential for creating ideal growing conditions for plants, involving precise control of temperature, humidity, C O 2 concentration, and light levels. Technological advances allow the implementation of both manual and automated systems that monitor and adjust these environmental factors in real-time, using sensors and actuators to maintain conditions within optimal ranges. Heating and cooling systems, forced ventilation, C O 2 injection, and supplementary artificial lighting are examples of technologies used to optimize the microclimate inside greenhouses. These technologies not only ensure the healthy growth and productivity of plants but also contribute to the sustainability of agricultural production, minimizing the use of natural resources and dependence on external climatic conditions.
The technologies used to regulate the temperature of greenhouses can be categorized into passive and active systems. Passive systems include elements such as heat storage walls [9]—which capture heat during the day and disperse it at night (Figure 7b)—and openings that promote natural ventilation (Figure 7a). On the other hand, active systems are made up of devices such as fans, heaters, underfloor heating systems and air conditioning units. They can be automated with temperature sensors to ensure stable conditions conducive to plant development.

3.1.3. Passive Heating and Cooling Systems

Passive heating and cooling systems in greenhouses are efficient and sustainable methods for controlling the temperature within these structures, taking advantage of natural resources and physical phenomena, without the need for active external energy sources such as electricity or fossil fuels. These systems are designed to maximize the use of solar energy, wind, and the thermal properties of the soil to create an ideal growing environment.
The north wall, seen in (Figure 7b) acts as a passive form of greenhouse heating technology, absorbing solar heat during the day due to high thermal capacity materials such as concrete, stone or barrels of water. This heat is gradually released at night, maintaining a more stable temperature. Its strategic location maximizes solar exposure in other areas of the greenhouse, reducing the need for external energy sources and contributing to sustainability. Furthermore, it promotes thermal stability, benefiting plant growth. This method, together with openings for natural ventilation, (Figure 7a), demonstrates how design strategies can be used to maximize thermal efficiency and promote sustainability in greenhouses, reducing dependence on external energy sources and optimizing plant growth.
The use of the north wall in agricultural greenhouses faces several limitations that directly impact its thermal and energy performance. Studies identify that the internal configuration of the wall, such as concavo-convex structures, significantly influences the heat storage and release capacity, with honeycomb walls presenting better thermal insulation and heat storage properties compared to flat walls [123,124]. Furthermore, the choice of materials, including the use of phase change materials (PCM), can improve energy efficiency, although the presence of thermally stable layers can reduce the heat storage capacity of the north wall [125,126]. Adequate thermal insulation of the north wall is also crucial to reduce heating demand, potentially reducing the need for external energy by up to 31.7% [87]. Therefore, optimizing the design and materials of the north wall is critical to maximizing the thermal efficiency and sustainability of agricultural greenhouses.
In addition to the strategic use of the north wall for passive heating, other complementary techniques are essential for optimizing passive heating and cooling systems in greenhouses. For example, the use of glass roofs or translucent materials allows sunlight to pass through, which is converted into heat inside the greenhouse, a principle known as the greenhouse effect. During the colder months, this helps keep the greenhouse warm without the use of active heating systems. To prevent overheating during the warmer months, automated or manual shading systems can be implemented.
Shading in agricultural greenhouses is a technique used to control the intensity of solar radiation that reaches plants grown inside the greenhouse. This can be conducted using different materials [91], such as shading screens as we can see in (Figure 8) [127] or special paints applied to greenhouse covers [128]. Shading may be necessary to reduce excessive solar radiation and, consequently, the temperature inside the greenhouse, especially in places with a hot climate. Furthermore, shading can help control plant transpiration, increasing relative air humidity and reducing water stress [129]. However, it is important to remember that shading can also negatively affect plant growth and development if the light intensity is reduced excessively [130,131]. Therefore, it is necessary to adjust the shading rate according to the specific needs of the cultivated plants and the climatic conditions of the region.
The field of shading in agricultural greenhouses is addressed by several studies, each exploring different methods and their implications for agriculture in regions of high solar radiation. Roslan et al. [132] investigated the application of dye-sensitized solar cells (DSSC) as shading in greenhouses, offering new perspectives for the manipulation of solar radiation. This study addresses the potential and effectiveness of DSSC as shading in greenhouses, evaluating its unique characteristics and advantages, especially in manipulating solar radiation through the optimal choice of photosensitizers. Additionally, the document consolidates materials used in manufacturing the DSSC for greenhouse shading, detailing photo-sensitization and light collection within the PAR wavelength for sustainable plant growth.
Figure 8. Greenhouses equipped with photovoltaic panels. (a) Greenhouse structure model equipped with normal photovoltaic panels; (b) Prototype greenhouse structure equipped with smart photovoltaic panels [133].
Figure 8. Greenhouses equipped with photovoltaic panels. (a) Greenhouse structure model equipped with normal photovoltaic panels; (b) Prototype greenhouse structure equipped with smart photovoltaic panels [133].
Applsci 14 06158 g008
The study by Lopez-Diaz et al. [134] addresses the impact of shading by photovoltaic panels in greenhouses oriented from north to south on tomato productivity in southeastern Spain. Evaluating three levels of shading (15%, 30%, 50%) and control without shading (0%), radiation, temperature, pH, substrate electrical conductivity, productivity and fruit quality were monitored. It was revealed that increased shading reduces available radiation, negatively affecting fruit productivity and quality, highlighting the need to optimize shading to balance agricultural production and energy generation.
The study by Moretti and Marucci [135] explores a prototype photovoltaic greenhouse with variable shading, designed to optimize agricultural production and energy generation. By rotating the solar panels, internal shading is adapted according to climatic conditions, maintaining internal radiation at ideal levels for cultivation. Analyzing solar radiation throughout the year, with clear and partially cloudy skies, the study demonstrates how adjusting shading can efficiently regulate the indoor microclimate, satisfying the needs of cultivated plants.
Maraveas et al. [133] carry out a literature review on smart and solar covers on greenhouse roofs, focusing on smart photovoltaic (PV) systems, optimization of power conversion efficiency (PCE), and integration of the Internet of Things (IoT) to improve sustainability and reduce operating costs of commercial greenhouses. The research addresses advances in IoT systems and artificial neural networks for automation, the use of quantum dots and semi-transparent organic solar cells to improve energy efficiency, and the potential for energy cost savings of between 40% and 60% through the replacement of conventional sources with solar energy. The limited commercialization of emerging innovations such as transparent and semi-transparent PVs is emphasized, but the significant role of smart materials and smart energy harvesting in improving thermal regulation, preventing frost, and managing pests and diseases is highlighted, culminating in lower post-harvest losses and better agricultural yields.
La Notte et al. [136] examine the integration of inorganic and organic photovoltaic (PV) technologies in greenhouses, highlighting the methodology of literature review, simulation studies and experimental work to evaluate energy, economic and environmental performances, in addition to the effects on crop growth. Explores the potential of organic solar cells, sensitized by dyes and perovskites, due to their semi-transparency and flexibility, allowing easy integration with existing or new greenhouse structures. The results highlight the great potential of these new PV technologies to promote sustainable, self-sufficient and intelligent greenhouses, with an emphasis on tuning the spectral characteristics of cells with the needs of plants, optimizing the use of solar energy and creating an environment conducive to crop growth, especially in hot and tropical regions.
The main limitations in the use of the north wall in agricultural greenhouses focus on aspects such as the effectiveness of heat storage and release, influenced by the internal structure and material of the wall. The concave–convex configuration of the wall can vary significantly in thermal performance, affecting heat storage capacity [123]. Honeycomb walls show better thermal insulation properties compared to flat ones, but the implementation of phase change materials (PCM) in the north wall, despite improving the thermal environment, faces the challenge of thermally stable layers that can reduce the thermal insulation capacity [126].
Shading in agricultural greenhouses, although effective in mitigating excessive heat in hot, sunny regions, has several limitations that include the significant reduction in solar radiation required for photosynthesis, negatively impacting crop development. Inadequate application of shading can lead to increased relative humidity, increasing the risk of fungal diseases, and uneven distribution of light within the greenhouse, affecting the uniformity of plant growth. Furthermore, shading strategies, such as the use of nets or reflective paints, can result in variations in internal temperature which, if not adequately managed with effective ventilation systems, may not provide the desired thermal relief or even increase thermal load. The correct selection and implementation of shading techniques, therefore, requires careful consideration of specific crop needs, local climatic conditions, and integration with other greenhouse microclimate management practices to ensure a balance between protection from excessive heat and the maximization of agricultural production [137,138,139].
On the other hand, ventilation systems play a crucial role in both heating and cooling systems in greenhouses, acting both passively and actively. Ventilation systems in greenhouses are essential for climate control, acting on both cooling and heating to maintain the ideal temperature for plant growth. In summer, they facilitate the expulsion of hot air and the entry of cooler air, while in winter, they help in the uniform distribution of hot air generated by heating systems. In addition to regulating temperature, ventilation is essential to remove excessive humidity, prevent plant diseases, and renew C O 2 , a vital nutrient for photosynthesis. Implementing ventilation, whether passive [140] or forced [141], is crucial to dissipate excessive heat, remove accumulated moisture, reduce the risk of fungal and bacterial diseases, and provide C O 2 necessary for photosynthesis.
Passive ventilation, also known as natural ventilation, as we can see in Figure 9a, takes advantage of wind forces and thermal differences to move air through the greenhouse, constituting a sustainable and economical option that does not require the use of electrical energy. Greenhouses designed for this type of ventilation have openings in the roof and sides, which can be adjusted manually or through automated systems to control the airflow [142,143]. Alternatively, active ventilation also called forced ventilation, seen in Figure 9b, uses fans and other electrical devices to control the internal conditions of the greenhouse, offering more rigorous control of environmental variables. These systems are often integrated with thermostats and hygrometers, allowing automatic adjustment of temperature and humidity to maintain optimal growing conditions [141].
When designing ventilation systems for greenhouses, it is essential to consider the local climate, the type of plants grown, and the orientation and construction of the greenhouse. Ventilation must be evenly distributed to avoid areas of air stagnation, adjustable to meet the specific needs of plants at various stages of growth and in different climatic conditions [144].
The article by Zhang et al. [145] reviews advances in greenhouse technology, focusing on environmental control and the effectiveness of natural ventilation. It discusses various models for evaluating ventilation in greenhouses, and factors influencing ventilation effectiveness, and suggests areas for future research to develop dynamic ventilation models for solar and tunnel greenhouses in China. Further investigations into the pressure distribution function in greenhouses, cross-ventilation models, and ventilation models considering plants are also proposed to increase the accuracy of these models and provide a comprehensive assessment of ventilation performance beyond the ventilation rate.
The study by Van den Bulck et al. [141], conducted over two years, monitored a compact ventilation concept in a semi-enclosed greenhouse in Belgium. The concept integrates intensive thermal screening with controlled ventilation, aiming for energy-efficient solutions amid rising energy costs and climate change pressures. The first year showed 9.6% higher energy consumption compared to a reference case, attributed to its location in the greenhouse. However, the second year demonstrated a potential energy saving of 12% with improved crop growing conditions, highlighting the importance of greenhouse climate control for optimizing agricultural yield.
Li et al. [146] examine the microclimate in single-slope greenhouses in Weifang, Shandong Province, China, focusing on temperature, humidity, solar radiation, C O 2 concentration, wind speed and vapor pressure deficit. It uses a quality conservation method to estimate ventilation volume and humidity growth after ventilation, revealing that changes in the internal environment of greenhouses are influenced by several factors, such as plant density, coverage, growing height and opening times of ventilation, providing insights to optimize ventilation strategies in greenhouses.
Xu et al. [140] propose an M-shaped greenhouse with improved wind resistance and ventilation efficiency, designed to offer superior wind resistance and ventilation performance. This study investigates the optimization of the V angle of the M-shaped greenhouse, comparing its performance in terms of wind resistance and ventilation efficiency with traditional large-arch greenhouses. The M-shaped greenhouse is found to have a higher ventilation efficiency and slightly lower temperature compared to traditional arc greenhouses, making it ideal for use in agricultural production, especially in locations prone to strong winds.
The study by Akrami et al. [73] uses computational fluid dynamics to study the effects of ventilation configuration in greenhouses. This study reveals the importance of the location and type of openings to optimize ventilation, crucial for maintaining ideal temperature and humidity conditions inside the greenhouse, aiming for sustainability and energy efficiency in agriculture. These studies collectively highlight the importance of efficient ventilation for the sustainability and effectiveness of greenhouses.
Figure 9. Greenhouses with different forms of ventilation. (a) Greenhouse with natural ventilation system [147]; (b) Greenhouse with natural ventilation system [147].
Figure 9. Greenhouses with different forms of ventilation. (a) Greenhouse with natural ventilation system [147]; (b) Greenhouse with natural ventilation system [147].
Applsci 14 06158 g009
Ventilation is, therefore, one of the main strategies for controlling temperature, humidity and gas concentration inside greenhouses, standing out as a focus of several studies that evaluate different systems and their impact on the growing environment and agricultural production. This technology is essential in the search for more efficient and sustainable cultivation methods, playing a key role in optimizing the environment for plant growth.
A passive heating and cooling system is especially effective when combined with the correct orientation of the greenhouse, allowing for maximum use of the prevailing breezes [69,148]. Additionally, using water through misting systems or water walls can promote evaporative cooling, efficiently reducing air temperature [9]. These strategies, combined with intelligent design and suitable materials, form an integrated system that promotes an ideal growing environment, minimizing the use of external energy and maximizing the sustainability of greenhouse operations.
The limitations of natural ventilation in agricultural greenhouses are marked by its direct dependence on external conditions, such as wind speed and direction, as well as the difference in temperature between the interior and exterior, which can result in an inadequate ventilation rate in days with little wind. Additionally, natural ventilation can lead to an uneven distribution of air within the greenhouse, creating zones of different temperatures that affect the uniformity of plant growth [149]. The use of insect screens, although beneficial for protecting crops from pests, can significantly reduce the ventilation rate due to resistance to airflow [150]. Furthermore, limited control over natural ventilation makes it difficult to maintain ideal growing conditions, especially during climate extremes, and the specific greenhouse configuration, including the type and location of ventilation openings, can vary significantly in effectiveness, requiring, e.g., Sometimes, complementary mechanical ventilation systems to optimize the growing environment [151].
Thus, passive heating and cooling systems represent a holistic and environmentally friendly approach to climate management in greenhouses, aligning with sustainable agricultural practices and reducing environmental impact.

3.1.4. Active Heating and Cooling Systems

As for active systems, they are characterized by the use of equipment and technologies that require external energy to operate, playing a crucial role in maintaining ideal conditions inside the greenhouse, regardless of external climate variations. Different active heating methods are adopted, each with its own particularities and efficiencies, including combustion, electric, hot water pipe systems, geothermal and solar heating.
Combustion heating involves the use of gas, oil or biomass as fuels. These are burned in a boiler, generating heat which is then distributed evenly throughout the greenhouse via ducts or radiators. This method allows for rapid adjustment of internal temperature, being effective in maintaining ideal growing conditions even in cold climates, as detailed by [152,153].
The electric heating system operates through resistances that convert electrical energy into heat. The advantage of this system lies in its ability to precisely control and uniformity of temperature distribution, facilitating the maintenance of a stable cultivation environment as pointed out by [154]. However, the operating cost can be high due to the price of electricity.
Heating through hot water pipes works by circulating heated water inside pipes that run through the greenhouse. This system disperses heat efficiently and evenly, ensuring an ideal temperature distribution for plant development. The water is heated externally and its circulation through the tubes allows direct heat transfer to the greenhouse’s internal environment [155].
The use of geothermal energy to heat or cool greenhouses takes advantage of the constant temperature below the Earth’s surface. For heating, geothermal systems capture heat stored underground to increase the temperature inside the greenhouse during the colder months. Using heat pumps and circulation systems, it is possible to extract this heat and use it to heat the interior of the greenhouse. This technology offers a sustainable solution with low operational costs, as it uses a renewable and constantly available energy source [10,11,156,157].
Solar heating, on the other hand, captures the sun’s energy through solar panels. These convert solar energy into heat, which is then distributed within the greenhouse. This system not only reduces dependence on fossil fuels but also minimizes the environmental impact of greenhouse cultivation [158,159]. It is a sustainable solution that takes advantage of a renewable and abundant resource to create an optimized growing environment, as illustrated in Figure 10a. Figure 10b highlights the practical application and effectiveness of geothermal technology in greenhouses, reinforcing its role as a viable and efficient alternative for sustainable greenhouse heating.
Hamdane et al. [10] investigated the impact of using ground-to-air heat exchanger (EAHE) systems in agricultural greenhouses, highlighting significant improvements in thermal performance and reductions in environmental impact. They experimentally found that EAHE can cut heating energy consumption by more than 40% and lower C O 2 emissions by more than 100 g/h. EAHE efficiency fluctuates with the operating mode (heating or cooling) and specific conditions, such as the difference between the target temperature and the air temperature emitted by the EAHE, directly influencing both energy consumption and C O 2 emissions.
As for cooling, there are ventilation systems to regulate the temperature, some accompanied by exhaust fans to improve air circulation, using the temperature difference between the inside and outside to create an airflow that reduces internal heat. Adjustable thermal curtains help control the entry of light and heat, using materials that reflect solar radiation or thermally insulate, preventing excessive heat from penetrating the environment [110]. Evaporative cooling uses water to absorb heat, a process in which water, when evaporating, removes heat from the air, thus reducing the temperature of the environment, while exhaust fans and fans ensure air circulation, promoting constant air renewal and efficiency in removing hot air [122,160].
In addition to the techniques, there are additional approaches such as shading and reflection that use special materials that reflect solar radiation to prevent overheating [161]. These materials, by reflecting sunlight, reduce the amount of heat that enters the greenhouse, thus maintaining the internal temperature at lower levels. Composite systems are also used, taking advantage of the constant temperature of the subsoil or groundwater to cool the air in the greenhouse [161]. These systems route air through underground pipes where, due to the lower temperature of the soil or groundwater, the air is cooled before being reintroduced into the greenhouse. Geothermal energy and passive radiative cooling are other techniques used, the first uses underground heat to heat or cool the greenhouse [162]. For cooling, the geothermal system can be used to extract heat from inside the greenhouse and dissipate it underground, taking advantage of the lower ground temperature to cool the internal environment. This is particularly useful in hot climate regions where excess heat can be detrimental to plant growth.
Radiative cooling uses materials that emit infrared radiation into space, cooling themselves in the process and reducing the temperature of the greenhouse [163]. Finally, solar cooling uses solar energy to operate absorption or adsorption cooling systems, offering a sustainable cooling alternative with low energy consumption [164].
These innovative techniques provide efficient and sustainable methods for maintaining the ideal environment within greenhouses, reducing the need for conventional energy and promoting more sustainable agricultural practices. And they can be used in combination to increase energy efficiency and reduce costs. It is important to choose the appropriate technology for each greenhouse, considering factors such as the local climate, greenhouse size, investment capacity and energy efficiency. The right choice can significantly reduce operating costs and improve crop production. The selection of climate control technology for greenhouses is a balancing act that considers local variables, accessibility to energy sources, the allocated budget and the specific requirements of the plant species grown.
Active heating and cooling systems face several limitations, such as high energy consumption, which significantly increases consumption rates in buildings, and integration challenges with the built environment, including complicated control, acoustic issues, and higher initial costs. Additionally, efficiency and thermal constraints limit the applicability of these systems, requiring active cooling solutions to provide a significant thermal advantage over passive options to be considered viable. These factors combined highlight the need for continued research to overcome these obstacles and develop more efficient systems that are well integrated into the built environment [165,166,167,168].
Efficient climate management in greenhouses is essential for reducing energy consumption and greenhouse gas emissions, as demonstrated in a series of recent studies. In a comprehensive overview of studies on energy efficiency in greenhouses. Paris et al. [169] carried out a detailed analysis of the greenhouse agricultural sector in the European Union (EU), emphasizing the dependence on fossil energy sources and the variation between high-energy systems in the north (predominantly for heating and cooling) and low-energy systems in the south (involving heating, cooling, irrigation, lighting, fertilizers and pesticides). They proposed the adoption of renewable energy sources and energy efficiency measures.
Soussi et al. [122] provided a comprehensive analysis of climate control and cooling systems in greenhouses under hot and arid conditions. They focused on technologies that reduce energy and water consumption, highlighting the importance of maintaining a suitable indoor environment in greenhouses to ensure high-yield crops with lower resource consumption. The in-depth review covers several cooling technologies, including systems that utilize heat exchangers, ventilation, evaporation, and desiccants and points to energy-efficient approaches such as desiccant dehumidification systems for greenhouse agriculture. Additionally, the study identifies future trends in cooling systems, which include water recovery through evaporation-condensation, and suggests opportunities for future research and development. This work highlights the complexity of environmental control in greenhouses and the need for innovative solutions to address the challenges posed by hot and arid climates.
The article by Ferraro et al. [170] explores the effectiveness of heat recovery systems in reducing the energy needs of greenhouses, particularly in winter. Through numerical simulations that incorporate the energy and mass balance, it was possible to demonstrate a significant annual reduction in the need for thermal energy, with energy savings reaching 45.6%. This study highlights the potential of such systems to improve the sustainability of agricultural production in controlled environments, emphasizing their importance in reducing energy consumption in greenhouses.
Tawalbeh et al. [171] investigate the efficiency of heat recovery systems to reduce energy demands in greenhouses, especially notable during winter. Using numerical simulations that consider energy and mass balance, the authors demonstrate a significant reduction in the annual consumption of thermal energy necessary to maintain appropriate levels of temperature and relative humidity. In particular, the research reveals that for the post-heating process, the traditional approach results in an energy consumption of 1,559,420 kWh, while the implementation of an RRU80 heat recovery unit leads to a reduced consumption of 848,342 kWh. This translates into an energy saving of 45.6%, highlighting the substantial potential of heat recovery systems in promoting the sustainability and energy efficiency of agricultural production in controlled environments.
Guan et al. [172] investigated the performance of an innovative greenhouse wall equipped with micro heat pipe arrays (MHPAs) and phase change materials (PCMs) in solar greenhouses, demonstrating its ability to significantly improve heat storage and release, compared to conventional walls. The integration of MHPAs and PCMs addressed the problem of low thermal conductivity of PCMs, increasing the efficiency of thermal storage. As a result, the experimental wall showed an increase of 95.35% in heat storage and 96.42% in heat release in relation to the common wall, contributing to maintaining the internal temperature of the greenhouse within an ideal range for plant growth, even at night and in adverse weather conditions. These findings highlight the importance of technological innovation in greenhouse climate management, with a particular focus on environmental sustainability.

3.1.5. Maintenance of Carbon Dioxide C O 2 and Light

Control of C O 2 and lighting constitute essential elements in the greenhouse environment, playing crucial roles in facilitating photosynthesis as well as optimal plant growth and development. These factors, along with careful regulation of temperature and humidity, form the basis for a growing environment that promotes plant health and productivity [173].
Effective management of C O 2 levels is vital to optimize photosynthesis and stimulate vigorous growth. Strategies to adjust the concentration of C O 2 involve the controlled injection of this gas, using advanced technologies that allow precise application, fundamental for the photosynthetic process [174]. Adequate ventilation is also essential, keeping C O 2 levels within optimal limits to avoid saturation or deficiency, which could adversely affect photosynthetic efficiency and plant development.
When it comes to light, careful management of its quantity, intensity and duration is necessary to meet specific plant growth requirements. As can be seen in (Equation (1)), light plays a critical role in agricultural greenhouses, acting as an essential catalyst in the process of plant growth and development.
The quality, intensity and duration of light in the greenhouse directly influence the rate of photosynthesis [175]. Light not only provides the energy necessary for photosynthesis but also regulates other important physiological processes, such as photomorphogenesis, which is the development of plants in response to the quality of light [175]. For example, different wavelengths of light can affect flowering, stem elongation, leaf development and other aspects of plant development.
In agricultural greenhouses, where environmental control is paramount, artificial lighting becomes an indispensable resource, often used to complement natural light. This type of lighting is essential to optimize plant growth and extend growing cycles, regardless of external light [175]. This is particularly important in regions with low sunlight or during periods with less natural light. The implementation of adequate lighting systems is essential to increase the photosynthetic efficiency of plants and, consequently, the productivity of the greenhouse [75,92].
Using techniques such as shading systems, supplemental lighting, curtain control and strategic crop positioning helps maximize light absorption, crucial for photosynthesis, without excessively increasing the greenhouse’s internal temperature [176].
The interaction between light and C O 2 highlights the complexity of managing these elements in greenhouses, indicating the need for a carefully controlled environment to support vital physiological processes and ensure healthy plant development. Therefore, the implementation of advanced technologies to control C O 2 and light is a critical aspect of modern agronomy, allowing the creation of ideal growing conditions that promote efficiency and sustainability in agricultural production in controlled environments [177].
Maintaining optimized C O 2 concentration is vital to maximizing plant photosynthetic efficiency, resulting in accelerated growth and greater agricultural productivity. C O 2 maintenance systems are employed to adjust C O 2 concentrations within greenhouses, using methods ranging from conventional solutions to technological innovations [178,179,180].
The most conventional method of C O 2 enrichment involves the use of propane or natural gas burners [178,181,182]. These devices burn fossil fuels to generate C O 2 , releasing it directly into the greenhouse environment. Although effective in increasing the concentration of C O 2 , this method requires careful ventilation to prevent the accumulation of harmful by-products of combustion [183].
A more controlled approach involves direct injection of compressed C O 2 from cylinders or storage tanks. This system allows for more precise regulation of C O 2 concentrations, automatically adjusting to the specific needs of the plants and the environmental conditions of the greenhouse [183].
Advanced technologies in C O 2 control include C O 2 recovery systems from industrial exhaust gases or fermentation processes. These systems filter and purify C O 2 before its introduction into the greenhouse, representing a sustainable approach by reusing C O 2 that would otherwise be emitted into the atmosphere [183,184].
The implementation of Wireless Sensor Networks (WSN) and artificial intelligence techniques, such as neural networks, significantly improves the monitoring and control of C O 2 concentration, allowing real-time adjustments to optimize growing conditions [185,186,187,188]. Biomass-based systems also offer a sustainable solution for generating heat and C O 2 , using agricultural waste as fuel [189].
The development and integration of C O 2 maintenance systems in greenhouses, from conventional methods to innovative solutions, are fundamental to creating an ideal growing environment. This diversity of systems allows flexible applications adapted to different needs and operational conditions, promoting photosynthetic efficiency, improving crop quality and contributing to more sustainable agricultural practices [190]. Effective implementation of these systems can vary based on greenhouse type, climate, and crop type.
The choice of the type of artificial lighting for greenhouses will also depend on the type of cultivation, the light needs of the plants, the energy cost and the availability of energy in the region [75]. Sodium vapor lamps ( N a ) are often used in greenhouses due to their high light efficiency and low operating costs, but in some crops, such as those that produce flowers and fruits, lighting with LEDs can be more efficient [191]. Additionally, the use of light sensors can help optimize lighting time and minimize energy consumption. Importantly, excessive lighting can negatively affect plant growth and increase energy consumption; therefore, the efficient use of artificial lighting is essential to ensure healthy plant growth and reduce operating costs.
Lighting in agricultural greenhouses is crucial to crop production efficiency and performance. Ref. [192] investigated the sustainability of photovoltaic greenhouses in Europe, analyzing agricultural production in greenhouses with different solar panel coverages and concluding that a 25% coverage is ideal for most crops, while low-light crops can tolerate up to 100% coverage with innovative cultivation systems. Katzin et al. [174] studied the transition from high-pressure sodium lighting to LEDs in greenhouses, observing energy savings of between 10% and 25%, depending on the energy previously used for lighting.
Paradiso and Proietti [175] and Afzali et al. [193] addressed the manipulation of light quality with LEDs to optimize plant growth and photomorphogenesis, highlighting the benefits of LEDs over conventional light sources and their potential to improve production in controlled environments. Serale et al. [75] developed a supervisory control strategy based on artificial intelligence to improve energy efficiency in artificial lighting systems, demonstrating a significant reduction in energy consumption and improvement in crop yields. Together, this research reveals the significant impact of advanced greenhouse lighting on the efficiency and sustainability of agricultural production.
The main limitations in the use of carbon dioxide ( C O 2 ) and light maintenance systems in greenhouses include the complexity of maintaining optimal concentrations of C O 2 due to its dynamic and delayed interaction with temperature, humidity, and light intensity, among others. This results in frequently suboptimal or excessive C O 2 concentrations, making it difficult to maximize crop productivity and quality. Additionally, efficient management of artificial light represents a significant challenge, as it requires a balance between providing sufficient light for photosynthesis and minimizing energy consumption. Therefore, optimizing the control of C O 2 and light in greenhouses requires integrated approaches that consider all these interdependent variables to achieve the desired energy efficiency and agricultural productivity.
Table 7, Table 8 and Table 9 synthesize recent studies series that exploit heating and cooling technologies, the influence of ventilation and shading on agricultural greenhouses and their impact on energy performance. These compilations highlight traditional and emerging technologies, including natural ventilation systems, thermal energy storage and use of renewable energy sources, focusing on efficiency and sustainability.

3.2. Water Resources in Greenhouses

Greenhouse water resources refer to the efficient management and use of water within controlled growing environments such as greenhouses. Although this topic is not the main focus of this article, and therefore, is not in-depth, it is important to provide a brief description of this topic.
Efficient management of water resources is essential for the sustainability of greenhouse agriculture, given the growing demand for food and water scarcity. The adoption of efficient irrigation systems and appropriate management practices can optimize water use, maximizing crop productivity with minimal environmental impact [199].
Intelligent irrigation systems, which provide water and nutrients precisely, avoiding waste, are essential for increasing water efficiency and crop yield, contributing to sustainable production [200].
The objective of irrigation in greenhouses is to maintain soil moisture at the ideal level, ensuring uniform distribution of water to all roots, without excess or lack [201]. Choosing the appropriate irrigation system and monitoring water quality is crucial to the success of production [202].
There are several irrigation systems that can be used in greenhouses, and the choice of system depends on the crop, climate and specific conditions of the structure [203,204].
Drip irrigation is efficient, allowing water and fertilizer savings, and is more than 90% efficient compared to traditional systems that have around 50% efficiency. Aerial sprinklers and micro-sprinklers distribute water by increasing humidity, helping to control diseases and pests and offer water savings of 50 to 70% over older methods [205,206,207].
Hydroponics is an advanced technique that provides efficient use of water and nutrients, although it requires more in-depth technical knowledge and a larger initial investment [208,209]. Furrow irrigation, despite its simplicity and low cost, is less suitable for greenhouses [210], while nebulization is used to increase the relative humidity of the air, requiring strict control to avoid humidity saturation [211,212]. Flood irrigation is rarely used in greenhouses due to the complexity of control. Underground irrigation, which applies water directly to the root zone, minimizes evaporation loss and improves water use efficiency [213,214,215]. Finally, hose irrigation or manual watering is more suitable for smaller greenhouses, despite being more labor intensive and offering a less uniform water distribution [216].
Among the main limitations of irrigation systems in greenhouses, implementation and efficiency challenges specific to each method stand out. Advanced systems such as hydroponics require significant investment and specialized knowledge for effective management of the nutrient solution, while traditional methods such as flood irrigation can lead to soil salinization due to inadequate control of the water applied [217]. Drip irrigation, although efficient in water use, requires careful maintenance to prevent clogging of emitters and ensure even water distribution [218]. Underground irrigation has the advantage of reducing emissions of nitrous oxide, a potent greenhouse gas, but its effectiveness depends on precise management of nitrogen and water [219].
Advanced research in greenhouse irrigation techniques focuses on optimizing irrigation efficiency and plant productivity by exploring a variety of systems, scheduling strategies, and advanced monitoring and control methods.
Studies such as Gultekin et al. [220] reveal that deficient irrigation practices in drip systems can increase greenhouse gas emissions, while strategies for more efficient use of agricultural land can help reduce them. This field of research covers both environmental implications and efficient management practices. On the other hand, works such as Nikolaou et al. [199] and Incrocci et al. [204] focus on improving irrigation management practices in greenhouses, with an emphasis on precision agriculture. They highlight the importance of sustainable strategies, including reducing nitrate leaching loss and implementing integrated pest and disease management practices.
At the same time, the study by Raudales et al. [213] investigated the cost of treating irrigation water in greenhouse production, highlighting variations in costs according to the type of filter and the origin of the water. This study emphasizes the economic importance of efficient selection and treatment of irrigation water. On the other hand, the study by Lucero-Vega et al. [221] evaluated the effectiveness of an underground irrigation system in reducing soil evaporation, finding that the underground irrigation system using diffusers (RSD) reduced evaporation by 30% compared to irrigation system located in ditches (RZ) and 44% in relation to the drip irrigation system with tape (RGC), highlighting the viability of this technique to increase water efficiency in agriculture. These studies reflect both the economic and environmental relevance of greenhouse irrigation practices.

4. Control Strategies in Greenhouses

Greenhouse control technology refers to the use of different devices and systems to monitor and regulate the environment inside greenhouses, aiming to optimize conditions for plant growth [222,223]. This can include the use of temperature, humidity and light sensors, automated watering systems, controlled ventilation and even the use of artificial intelligence to optimize the growing environment. These technologies aim to maximize production, improve crop quality and reduce the use of resources such as water and energy [224,225].
Just as water resources are not the main focus of this article, control strategies in greenhouses are also not part of the main scope of the discussion but deserve a brief approach.
In greenhouse management there are mainly two forms of control, which are distinguished by the degree of automation and technological complexity, these two forms of control are manual control, which involves direct human intervention, and automated control, which uses technology to monitor and adjust environmental conditions, as we can see in the Figure 11.
In Manual Control, adjustments to the greenhouse environment, such as temperature, humidity, ventilation and irrigation, are made manually by the operator. This may include opening and closing windows or vents, turning heaters or fans on and off, and adjusting irrigation based on observation. The main advantages of this form of control are simplicity and lower initial cost. It is more adaptable to smaller greenhouses or where advanced technology may not be economically viable. On the other hand, it requires more labor, is less accurate and more susceptible to human error. May not be time and resource-efficient, especially in large-scale greenhouses [227].
In Automated Control, a variety of sensors and computer-controlled devices are used to continuously monitor and adjust the environmental conditions of the greenhouse [228]. These include automated irrigation systems, temperature and humidity control with automated ventilation and heating, controlled lighting and in some cases, automatic C O 2 enrichment. The main advantages of this form of control are greater precision in environmental control, time and labor savings, and the potential to significantly improve efficiency and productivity. Automation allows for constant, real-time monitoring and adjustment, adapting to changes in external and internal conditions. On the other hand, it requires higher initial installation and maintenance costs [229,230]. Requires technical knowledge to operate and maintain the system.
The choice between manual and automatic control in greenhouses depends on several factors, such as the size of the operation, the type of cultivation, the available budget, and the desired level of precision and efficiency. While smaller, less technological greenhouses can operate efficiently with manual control, larger, more intensive operations often benefit significantly from automation.
Control technology in greenhouses encompasses various forms of automation and monitoring, including Sensor Networks for real-time monitoring and accurate data, the use of Artificial Intelligence to optimize the environment and predict problems, Automated Irrigation Systems for efficient use of water, Automated Ventilation Control for temperature and humidity regulation, Light Control Technology for maximizing photosynthesis, Automatic Control Approaches with algorithms and continuous feedback [230,231].

4.1. Sensors Used in Greenhouses

In greenhouses, different types of sensors are used to monitor and control environmental conditions, ensuring healthy plant growth. These sensors are essential for the automation and optimization of processes within the greenhouse. Below are the main sensors used in greenhouses:
  • Temperature Sensors: Monitor air and soil temperatures inside the greenhouse, allowing automatic adjustments to maintain optimal conditions for plant growth [232].
  • Humidity Sensors: Measure relative air humidity and soil moisture, helping to ensure that plants receive the appropriate amount of water [233].
  • Light Sensors: Monitor the intensity and duration of light received by plants, helping to control artificial lighting to optimize photosynthesis [234].
  • C O 2 sensors: Measure carbon dioxide levels in the environment, which is crucial for photosynthesis and plant growth [235].
  • pH and EC (Electrical Conductivity) sensors: Monitor the pH and electrical conductivity of the nutrient solution, ensuring that plants receive nutrients appropriately [236].
  • Wind and Precipitation Sensors: Used mainly in greenhouses with natural ventilation, they help adjust ventilation openings based on external conditions [237].
  • Pest Presence Sensors: Detect the presence of pests and diseases, allowing quick and effective intervention [238].

4.2. Actuators Used in Greenhouses

In greenhouses, several actuators are used to automatically regulate the environment, ensuring optimal conditions for plant growth. These devices work in conjunction with sensors to keep the greenhouse’s internal environment under control. Below are the main actuators used in greenhouses:
  • Automated Irrigation Systems: Control the quantity and frequency of irrigation based on soil moisture data [239].
  • Fans and Ventilation Systems: Regulate air circulation and ventilation within the greenhouse to maintain adequate temperature and humidity [240].
  • Heaters: Maintain the ideal temperature in cold climates, activating automatically when the temperature drops below a certain level [241].
  • Shading Systems: Control the amount of sunlight that enters the greenhouse, protecting plants from excess light and heat [242].
  • Grow Lamps: Provide supplemental lighting, especially during periods of low natural light, to ensure plants receive enough light for photosynthesis [243].
  • C O 2 dosers: Adjust carbon dioxide levels in the environment to optimize plant growth [244].
  • Automatic Ventanas and Blinds: Automatically adjust to control ventilation and sunlight based on sensor data [235].
Many of these sensors and actuators are integrated into automated control systems that use algorithms and artificial intelligence to optimize the greenhouse environment in real-time. These systems can automatically adjust environmental parameters based on collected data, ensuring optimal conditions for plant growth and increasing operational efficiency [234].
Control technology in greenhouses encompasses various forms of automation and monitoring, including sensor networks for real-time monitoring and accurate data, use of artificial intelligence to optimize the environment and predict problems, automated irrigation systems for efficient water use, automated ventilation control for temperature and humidity regulation, light control technology to maximize photosynthesis, and automatic control approaches with algorithms and continuous feedback [245].

5. New Technologies in Greenhouses

The study of new technologies in greenhouses is an expanding area, reflecting the ongoing effort to increase efficiency, sustainability and productivity in protected agriculture. Technological innovations applied to greenhouses address several challenges, including optimizing water use, climate control, automation and nutrient management.

5.1. Computational Modeling and Simulation

The modeling and simulation of greenhouse environments emerge as fundamental technologies for improving agricultural production in controlled environments. These tools allow researchers and growers to understand and optimize conditions within greenhouses to maximize energy efficiency and promote optimal plant growth. Using mathematical algorithms and advanced computational models, scientists are able to map and project the behavior of key variables such as solar radiation, relative humidity and temperature, and their synergistic effect on plant development [246,247]. These models act as digital simulacra of the greenhouse environment, providing a platform to test hypotheses and management strategies in a virtual context before their application in the real world [248]. Furthermore, they allow an in-depth analysis of the cultivation microclimate, revealing complex interactions between biotic and abiotic factors. Simulations include modeling light and shading distribution, optimizing microclimates for crops, and strategies for managing water resources, such as evapotranspiration. These tools are essential for increasing production efficiency, and sustainability and optimizing the use of resources in greenhouses.
Researchers use simulation to understand how greenhouses absorb, retain and dissipate heat, information that is crucial to improving energy efficiency and ensuring optimal conditions for plant growth [249]. Detailed mathematical models represent the dynamics of heat transfer, as well as biological processes such as photosynthesis and transpiration, which are directly influenced by thermal conditions.
With the integration of real-time meteorological data and the use of numerical simulation and finite element analysis techniques, the models are able to predict the behavior of greenhouses under a variety of environmental conditions, including climate extremes [250,251]. This allows for the implementation of automated climate control systems that actively adjust the greenhouse’s internal environment, such as humidity and temperature, to optimize productivity and resource efficiency.
Computer modeling and simulation in greenhouses have become invaluable tools in contemporary agriculture. They not only drive productive efficiency and sustainability but also equip agronomists with a deeper understanding and control over the growing environment, making the practice of agriculture more of an exact science than an art based on intuition [248].
In greenhouse modeling and simulation, precision agronomy benefits from a series of advanced technological tools. This includes computer modeling software to create detailed digital representations of the greenhouse environment, mathematical algorithms to simulate various environmental conditions, and finite element analysis to understand heat transfer and fluid dynamics. Sensors and monitoring systems are essential for collecting real data, which are integrated with real-time meteorological information, improving the accuracy of simulations. These tools, along with intuitive graphical interfaces and automated control systems, allow technicians to optimize the greenhouse microclimate, efficiently manage water resources, and implement more effective and sustainable growing strategies.
Greenhouse modeling and simulation face several limitations, including the complexity in predicting the performance of greenhouses heated with heat pipe systems, the challenge of capturing the dynamic behavior of the indoor environment due to variables such as design and weather conditions, the optimization of moving vessels to mitigate the effects of microclimates, the difficulty in creating models based on physical processes with parameters obtained from real data, and the execution of dynamic models to simulate heat transfer in naturally ventilated greenhouses. Furthermore, experimental validation under different environmental conditions to ensure the applicability of the models represents an additional challenge [252,253,254,255].
Some of the main technologies and approaches used include Computational Fluid Dynamics (CFD), which is widely used to simulate airflow, heat transfer, and C O 2 distribution within greenhouses. It allows a detailed analysis of the internal environment in three dimensions, considering complex climatic variables. CFD is used to study natural and mechanical ventilation, optimize greenhouse design, and analyze the impact of different environmental conditions [256]. A study used CFD to analyze the aerodynamic environment of a three-span greenhouse, highlighting the importance of thermal curtains to prevent heat loss in winter [257]. Finite Element Methods (FEM) and Finite Volume Methods (FVM) are both used to solve heat and mass transfer equations, but they differ in the space discretization approach. FVM is often more efficient in terms of computational time and memory usage compared to FEM. Both were compared for simulations of natural ventilation in greenhouses, with FVM showing better results in terms of accuracy and efficiency [256]. A study compared the efficiency of different discretization methods, including FEM, for simulating natural ventilation in greenhouses. FEM was used to model airflow and temperature distribution within a greenhouse, considering the complexity of the structure and ventilation openings. The study showed that although FEM required greater processing power and memory, it provided detailed results on the distribution of climate variables, which are crucial for optimizing greenhouse design and ventilation strategies [256]. Another study used FVM to simulate the internal environment of a semi-enclosed Venlo-type greenhouse equipped with air conditioners. The CFD model developed with FVM was able to predict the distribution of temperature, water vapor, and C O 2 inside the greenhouse. The simulation results showed good agreement with experimentally measured data, allowing a detailed analysis of the effectiveness of heating and ventilation systems, as well as the impact of different leaf area densities (LAD) on the greenhouse microclimate [258].
Models that simulate heat and mass transfer are essential for understanding the microclimate inside greenhouses. They use equations that consider sensible and latent heat fluxes, radiation, and plant evapotranspiration. These models help predict the distribution of temperature, humidity, and C O 2 , allowing the optimization of the position of heating and ventilation systems [258]. The study by Taki et al. [259] illustrates how dynamic heat and mass transfer models can be used to predict and control the indoor environment of greenhouses. The use of thermal screens, together with accurate modeling, can lead to a significant reduction in energy use and optimization of the microclimate, resulting in greater efficiency and sustainability in protected agricultural production.
The integration of photovoltaic panels in greenhouses to generate renewable energy while optimizing the internal microclimate through solar tracking systems is another advanced approach. Simulations have shown that shading-free solar tracking can significantly increase power generation and improve global irradiance uniformity within the greenhouse [260].
Modeling based on Artificial Intelligence (AI) techniques such as neural networks and machine learning algorithms are also employed to model and control complex climate variables that are difficult to model with traditional techniques. A recent example of the use of AI techniques for climate modeling and control in greenhouses is the study by Charaf Eddine Lachouri et al. [261]. This study proposed an adaptive system based on artificial neural networks integrated with fuzzy logic, known as the Adaptive Neuro-Fuzzy Inference System (ANFIS). The objective was to predict air humidity, air temperature, internal radiation, and C O 2 concentration during the growth of tomato seedlings in a greenhouse. The simulations showed that the ANFIS model was able to efficiently predict the greenhouse’s internal conditions, helping to maintain an ideal environment for plant growth.
Figure 12 illustrates the computational analysis of temperature distribution and airflow patterns in a greenhouse, crucial elements for efficient climate control in protected growing environments. The top image uses a color scale to represent temperature variation, highlighting hotter and colder areas. The lower image shows the behavior of the airflow, which directly influences the circulation of heat and the exchange of gases inside the greenhouse. Understanding these standards allows you to optimize the design and operation of greenhouses to improve plant growth, energy efficiency and the overall sustainability of agricultural practices.
Many researchers address the topic of thermal modeling and greenhouse simulation, mainly in the areas of agricultural engineering, environmental sciences and applied physics. Iddio et al. [229] addresses the importance of controlled environment agriculture, with a focus on greenhouses, for constant agricultural production throughout the year. It is noteworthy that greenhouses, due to their lightweight construction and often inefficient operation, consume significant amounts of energy, mainly from fossil fuels, resulting in large carbon footprints. The article reviews strategies to improve energy efficiency in greenhouse operations, addressing the optimization of the control of essential environmental parameters and the implementation of advanced energy simulation technologies. The roles of monitoring systems and control algorithms, in addition to energy modeling, are also discussed as key tools for reducing energy consumption and carbon emissions in greenhouses, pointing out potential areas for future research aimed at a more sustainable and efficient operation.
Molina-Aiz et al. [256], discussed the efficiency of two different discretization methods used as computational fluid dynamics (CFD) solvers for simulating natural ventilation in greenhouses, focusing specifically on the Finite Element Method (FEM) and the Finite Volume Method (FVM). They compare the accuracy and computational efficiency of two simulation codes based on FEM and FVM for incompressible two-dimensional turbulent flow in naturally ventilated greenhouses. The study includes comparisons with experimental data in three types of greenhouses to evaluate the effectiveness of these CFD models. He discusses computational capacity, including required computation time and memory storage, finding that FEM required twice as much computation time per cell and step as FVM, with approximately 10 times greater memory storage needs.
The article by Ghani et al. [248] explores the thermal performance of an evaporatively cooled greenhouse in Qatar, considering three design factors: greenhouse geometry, operational parameters and geographic location. Geometric parameters included fan elevation, roof shape and aspect ratio; operational parameters focused on airflow rate and location factors evaluated incident solar intensity. The study used Computational Fluid Dynamics (CFD) to model a typical greenhouse according to ASHRAE standards, with simulations validated against measured data on internal air temperature, relative humidity and global solar radiation. The results indicated that positioning induction fans at or below crop height and doubling the ventilation rate from 20 Air Changes Per Hour (ACH) to 40 ACH effectively reduced the air temperature inside the greenhouse. The temperature rise induced by solar radiation was mitigated by increased ventilation rates, with the irregular span roof shape resulting in the lowest average indoor temperature. The effects of aspect ratio were negligible for greenhouses with the same floor area and volume.
Al-Helal et al. [11] explore the potential of geothermal energy for heating and cooling greenhouses in arid regions, highlighting the importance of determining the optimal depth for installing Earth-to-Air Heat Exchanger (EAHE) tubes. Through measuring the underground temperature over the course of a year and modeling and simulating the system, it was identified that a depth of 3 m is ideal, providing soil temperatures of 32 °C in summer and 29 °C in winter, providing a maximum cooling/heating capacity of 1000/890 MJ per day for every 1 m3 of humid air exhausted from a greenhouse, which provides a significant cooling/heating capacity. The research concludes that the condensation of water vapor in EAHE tubes would be unfeasible during cooling in a closed system, highlighting the viable potential of geothermal energy to improve energy efficiency and sustainability in greenhouses located in arid regions.
Cherrad et al. [157] investigated the ventilation and cooling of a tunnel greenhouse using geothermal energy in a desert climate. Using the Trnsys software for energy analysis and the Ansys CFD code to simulate internal thermal behavior, it was discovered that vents and chimneys help to evacuate hot air, improving cooling. The integration of geothermal energy has shown a significant reduction in internal temperature and cooling energy demand under critical climatic conditions, requiring only 5 kWh daily to cool the greenhouse to an ambient temperature of 37 °C, with the possibility of reducing energy consumption by up to 50% for ambient temperatures below 30 °C.
Table 10 presents a significant systematic review of the advances and applications of innovative technologies in agricultural greenhouses, highlighting the contribution of several studies in optimizing the greenhouse environment through the application of various techniques and methodologies. Each study listed addresses different aspects of protected agriculture, from energy efficiency and thermal performance modeling to the use of geothermal energy for temperature control in arid climates.
These works demonstrate the importance of thermal modeling and simulation of agricultural greenhouses to improve greenhouse performance and efficiency and suggest that the use of advanced modeling techniques can be a valuable tool for optimizing cultivation in controlled environments.

5.1.1. Benefits of Computational Modeling and Simulation in Greenhouses

Computer modeling and simulation offer several advantages for the operation and management of greenhouses. These advanced methods allow for a more detailed and accurate understanding of internal conditions, enabling adjustments and optimizations that would be difficult to achieve with traditional methods alone. The main benefits are as follows:
  • Optimizing Energy Efficiency: Optimizes energy consumption in greenhouses, resulting in a more sustainable and economical operation. It predicts the thermal behavior of the greenhouse and allows adjustments that reduce energy use [43].
  • Improved Microclimatic Control: Allows you to accurately predict and control microclimatic conditions inside the greenhouse, including temperature, humidity and C O 2 concentration, which improves plant growth and health [258].
  • Development of Accurate Models: Computational modeling allows for the creation of detailed models that consider several factors, such as solar radiation, airflow and evapotranspiration, providing an accurate prediction of the greenhouse’s internal environment [194].
  • Greenhouse Planning and Design: Computer simulation helps in the planning and design of greenhouses, allowing the evaluation of different configurations and materials to optimize energy performance and growing conditions [45].

5.1.2. Challenges and Considerations in Greenhouses

Despite the many benefits provided by computational modeling and simulation in greenhouses, there are also several challenges and important considerations to be taken into account. These challenges can impact the implementation and effectiveness of these advanced systems, requiring careful attention and proper planning. The main challenges are as follows:
  • Complexity and Initial Cost: Implementing computational modeling systems can be complex and expensive, requiring advanced hardware and software, as well as qualified professionals to configure and operate the models [263].
  • Maintenance and Update: Computer models and simulation systems require regular maintenance and technology updates to ensure ongoing accuracy and efficiency. This includes sensor calibration and algorithm updating [254].
  • Qualification and Training: Operators and managers need to be trained to understand and effectively use modeling and simulation technologies. Lack of training can lead to underutilization of these advanced tools [264].
  • Data Integration: Collecting and integrating accurate, real-time data from sensors and other devices is crucial to the effectiveness of models. Any discrepancies in the data can affect the accuracy of simulations and subsequent decisions [265].
These benefits and challenges highlight the importance of computational modeling and simulation in optimizing greenhouse operations, providing a solid foundation for developing more efficient and sustainable agricultural practices.

5.2. Automation of Greenhouse

Within the field of precision agronomy, the automation of greenhouse environments also represents another significant advance in optimizing plant growing conditions. With automation, it is possible to improve the efficiency and productivity of greenhouses, reduce costs and minimize environmental impacts [144,227,266], prevent and control diseases and pests, in addition to reducing the use of pesticides and other chemicals [267,268]. Automation in greenhouses is also important for managing data systems to collect and analyze crop information, allowing farmers to make more informed and accurate decisions [227,267,269]. Currently, automation in greenhouses is being increasingly adopted in different parts of the world, especially in regions with challenging climatic conditions [270].
However, it is important to highlight that automation in greenhouses still faces challenges in terms of costs and adaptation to local conditions [227]. Some studies also highlight the importance of ensuring the security of automation systems and protecting the information collected by data management systems [271,272]. Within the automation of agricultural greenhouses, several technologies stand out for their effectiveness, including environmental control systems, which adjust temperature, humidity and ventilation to create ideal conditions for plant growth; automated irrigation systems, which regulate the quantity and frequency of water according to the specific needs of the plants; lighting systems, essential for adjusting the intensity and duration of light, especially in places with insufficient natural light; pest and disease monitoring and control systems, which detect and combat phytosanitary problems efficiently, reducing the need for chemical products and data management systems, which collect, store and analyze crop data, allowing farmers to make decisions based on accurate and up-to-date information.
In addition to the topics presented, research on automation in greenhouses covers the use of robots to perform specific tasks, including harvesting fruits and vegetables, an innovation that promises to transform traditional agricultural practices [273]. Another significant advance is the integration of drones into greenhouse operations, which have become valuable instruments for aerial monitoring, analyzing plant health via multispectral images, and performing spraying and nutrient application tasks [274,275].
Some of the main aspects and technologies involved in greenhouse automation include sensors that monitor various environmental variables such as temperature, humidity, C O 2 levels, light, and soil moisture. These sensors capture data and transmit them to a central system that analyzes this information in real-time. Based on the data received, the system automatically adjusts the internal conditions of the greenhouse to ensure an optimal environment for plant growth [276,277]. In the study conducted by Ramasamy et al. [277], an innovative system was developed that uses a soil moisture sensor to monitor moisture levels and automatically activate a water pump whenever the soil is dry. This system, designed with high efficiency, can turn off the water pump as soon as soil moisture reaches a satisfactory level. Such technology is particularly useful in agriculture, as it significantly contributes to increasing crop productivity and reducing the need for manual labor. In this way, the system not only optimizes the use of water but also relieves farmers from the task of constant irrigation, allowing for the more effective management of agricultural activities.
Automated climate control systems utilize fans, heaters, cooling systems, and shade screens that activate automatically based on sensor readings. For example, if the internal temperature exceeds a pre-established threshold, fans or cooling systems are activated to reduce the temperature. This helps maintain stable environmental conditions, regardless of external variations [266]. In the study conducted by Salman et al. (2024), a thermoelectric air conditioning system that uses solar energy and is based on the Peltier effect was investigated. This system was specifically designed to cool a small test room with a volume of 1 m3. The results showed that the system has high cooling efficiency, maintaining the outlet temperature between 22 and 23 °C. This performance indicates the potential of the thermoelectric system as a viable and efficient alternative for cooling small environments, offering a sustainable and technically advanced solution for thermal control [278].
Automated irrigation systems use soil moisture sensors to determine plants’ water needs. When the soil reaches a moisture level below ideal, the system activates irrigation pumps that deliver the exact amount of water needed. This avoids wasting water and ensures that the plants are always adequately hydrated [279]. In the study by Lin et al. [280], a new one-sided nuclear magnetic resonance (UNMR) sensor was proposed for soil moisture detection. This sensor is based on a simplified analytical model that allows in situ detection of soil moisture with high efficiency and precision. The innovation lies in the sensor’s ability to perform continuous monitoring of soil conditions, which is crucial for agricultural and water resource management applications. The implementation of this sensor promises to significantly improve irrigation and soil management practices, ensuring a more sustainable and effective use of natural resources.
Fertigation combines irrigation with the application of nutrients. Automated systems control both the amount of water and the dosage of fertilizers applied to the soil. Based on the specific needs of plants at different growth stages, the system adjusts the composition and quantity of nutrients supplied, ensuring precise and efficient nutrition [281,282]. In the study conducted by Omara [283], the laser irrigation system (LSIS) was evaluated for its efficiency in water use and corn productivity. Operating at low pressure and simulating gentle rain, LSIS has been shown to be significantly more efficient in terms of water use and agricultural productivity compared to traditional irrigation methods. This technological innovation allows for a more uniform distribution of water, reducing waste and improving plant growth, which represents an important advance for sustainable agriculture and the efficient management of water resources.
Adjustable LED lighting is used to control the duration and intensity of the light. Light sensors measure the amount of natural light and, if necessary, artificial lighting systems are activated to supplement sunlight. This is crucial for plants that require specific light conditions for photosynthesis and growth. Automation allows you to adjust light and dark cycles to optimize plant development [276]. The paper by Mohagheghi and Moallem [284] presents the development of a low-cost sensor intended to measure the intensity of light required for plant growth, known as photosynthetic photon flux density (PPFD). This system was tested in a hydroponic test bed situated within a greenhouse, where it was exposed to both natural light and supplemental LED lighting in the blue and red light ranges. The results indicated that the sensor is effective in monitoring PPFD, providing accurate data to optimize lighting conditions, and improving plant growth and productivity in controlled environments.
Robots are used to perform repetitive tasks such as planting, pruning, harvesting, and transporting products within the greenhouse. Equipped with cameras and sensors, these robots can navigate the greenhouse, identify plants that require specific care, and perform the necessary tasks accurately and efficiently, reducing the need for manual labor and increasing productivity [285]. The article by Mujtahidin et al. [286] discusses the application of an advanced automated monitoring and control system in greenhouses. This system, which improves the traditional hydroponic method, includes the use of robots to monitor and control elements important for healthy plant growth. The integration of automatic control and monitoring technology via smartphone allows growers to maintain ideal pH levels and other critical factors efficiently and user-friendly. The application of these robots in greenhouses guarantees an optimized growing environment, promoting more robust and consistent plant growth, in addition to facilitating the management of essential parameters.
Management software integrates all automated systems into a centralized platform, allowing real-time control and data analysis. These platforms use artificial intelligence algorithms to analyze collected data and provide insights into greenhouse performance. Managers can monitor all aspects of the operation and make informed decisions to optimize cultivation and resources [287]. The study by Wang et al. [288] evaluates the effectiveness of integrated soil and crop management practices with the aim of improving productivity and environmental outcomes in an intensive greenhouse tomato production system in the Yangtze River Basin, China. The results indicate that the integrated soil-crop system management treatment led to a significant increase in fruit yields, ranging from 13.4% to 37.3% compared to other traditional practices. Furthermore, this integrated approach has resulted in lower greenhouse gas emissions, highlighting its benefits not only for agricultural productivity but also for environmental sustainability.
Internet of Things (IoT) devices enable communication between sensors and control systems, providing remote monitoring and control of the greenhouse. These devices collect data in real-time and transmit it to a central platform, facilitating the integrated management of the greenhouse environment [177]. The article by Lin et al. [289] proposes the StrawberryTalk platform, an innovative Internet of Things (IoT) solution for image-based disease detection in strawberries. The platform uses wall-mounted monitoring cameras that capture images of plants and eliminate blurred photos caused by wind, significantly improving accuracy in disease detection. With this technology, the system can identify all infected plants with high accuracy, providing an effective method for monitoring the health of strawberry crops and facilitating early intervention to control the spread of disease.
Artificial intelligence (AI) algorithms analyze sensor data to predict future conditions and automatically adjust control systems. AI can be used to predict irrigation needs, adjust lighting, and control climate based on historical data and weather patterns [266]. The study of Castañeda-Miranda et al. [290] proposes an intelligent system for frost prediction in greenhouses, integrating Internet of Things (IoT) technologies and hybrid Artificial Intelligence (AI) methods. Artificial Neural Networks (ANN) and a fuzzy control system are used to predict indoor temperatures and trigger water pump output levels for anti-frost irrigation control. The system consists of a climatological station equipped with ANN and fuzzy associative memory (FAM) for ecological irrigation control. ANNs provide an indoor temperature prediction with an efficiency greater than 90%, validated by the coefficient of variance ( R 2 ) analysis method. This intelligent system significantly improves frost forecasting and irrigation management in greenhouses, ensuring an ideal environment for plant growth.
Drones equipped with cameras and sensors monitor large areas of greenhouses, providing a detailed view of the state of plants and the environment. These drones capture images and data that are analyzed to detect problems such as pests or diseases [285]. The article discusses the use of hexarotor drones in greenhouse environments, exploring their application possibilities. Drones, or unmanned aerial vehicles (UAVs), are increasingly used for direct or indirect data collection. GPS navigation cannot be applied indoors. An alternative is to measure distance from a fixed set of sensors with known positions. The paper presents a dynamic model for a hexarotor drone and develops a new navigation algorithm based on a two-dimensional robot motion control algorithm. The aim is to ensure reliable drone navigation in a three-dimensional indoor environment. To prove the hypothesis, the simulation was implemented in the Scilab environment, demonstrating the effectiveness of the algorithm in drone navigation in greenhouses [291].
Renewable energy systems, such as solar panels, provide sustainable energy for greenhouse operations. Solar panels capture solar energy and convert it into electricity to power climate control systems, lighting, and other devices [266]. The comprehensive review by Cuce et al. [292] analyzes several energy-saving strategies and climate control technologies applicable to greenhouses. Highlighted technologies include photovoltaic (PV) modules, solar thermal collectors, and PV/T hybrid systems. The study reveals that, through appropriate retrofits, it is possible to achieve energy savings of up to 80%. These improvements not only reduce energy consumption but also contribute to the environmental sustainability of greenhouse operations, promoting more efficient and environmentally friendly agricultural production.
Technologies such as automatic traps and disease detection sensors monitor and control the presence of pests and diseases. Sensors detect signs of pests and diseases and trigger control measures, such as the release of biological agents or the application of specific pesticides [279]. The study by Dalai and Senapati [293] proposes an innovative pest detection system based on intelligent vision, using Regional Convolutional Neural Networks (RCNN) and deep learning. This system aims to control pests and diseases biologically, using computer and internet technologies instead of pesticides, to protect agricultural crops. Automatic plant pest detection has demonstrated significant improvements over traditional methods, offering a more effective and sustainable solution for pest management. This approach contributes to reducing the use of chemicals, promoting healthier and more environmentally friendly agriculture.
Figure 13 illustrates the practical application of monitoring sensors on cucumber plants inside a greenhouse. These devices are essential for collecting real-time data on plant health and development, enabling accurate and timely interventions to optimize growth.
Benefits of Automation in Greenhouses
  • Increased Productivity: Controlled conditions allow for faster cultivation cycles and higher yields [266].
  • Efficient Use of Resources: Reduced waste of water and nutrients, in addition to lower energy consumption [279].
  • Sustainability: Lower environmental impact due to efficient use of resources and reduced need for pesticides [285].
  • Quality and Consistency: Producing high-quality food with less variability [266].
Challenges and Considerations When implementing automation technologies in greenhouses, several challenges and considerations must be taken into account to ensure the successful integration and operation of these systems:
  • Initial Cost: Implementing automated systems can be expensive, but the long-term benefits often justify the investment [294].
  • Maintenance and Updates: Automated systems require regular maintenance and technological updates to remain efficient [225].
  • Training: Operators and managers need to be trained to use and maintain automation technologies effectively [287].
Automation in greenhouses is transforming agriculture, enabling producers to meet the growing demand for food sustainably and efficiently. These emerging technologies are at the heart of current research aimed at optimizing the production of high-quality food, demonstrating the transformative potential of automation in protected agriculture.

6. Building More Efficient Greenhouses in Diverse Climates

After a series of research carried out during the preparation of this article and based on the knowledge acquired, we present the following recommendations to improve the energy performance of agricultural greenhouses in different climates. It is essential to consider aspects such as design, materials, climate control, and technological innovations. These recommendations are based on recent articles and studies carried out throughout this work.
Flexible greenhouse design can greatly enhance adaptability to various climates. A modular arched shape, for instance, can be adjusted to different climatic conditions, making it both economical and versatile. In cold climates, a tighter arch helps retain heat, while in hot climates, higher arches facilitate ventilation and cooling. Additionally, greenhouses with adjustable roofs can maximize sunlight capture in winter and minimize heat input in summer, adapting to seasonal and geographic variations.
The use of climate-adaptable materials is another key consideration. High-efficiency glass or insulating polymers that provide high light transmission and excellent thermal insulation are crucial. Low-emissivity glasses are ideal for cold climates, while UV-resistant polymers are more suitable for hot climates. In cold regions, employing double or triple covers with layers of air between them can reduce heat loss, while in hot climates, these covers can be treated to reflect excessive solar radiation.
Integrated climate control technologies are essential for maintaining optimal conditions inside greenhouses. The combination of natural and forced ventilation, including windows, openings, and fans, can optimize air circulation and maintain adequate temperatures in temperate and hot climates. Geothermal heating and cooling systems are effective for both heating greenhouses in cold climates and cooling them in hot climates, utilizing stable underground temperatures to reduce energy consumption. Automated climate control systems using sensors and AI for real-time monitoring and adjustments are crucial. These systems can control irrigation, ventilation, and C O 2 enrichment with high precision, quickly adapting to external climate changes.
Technological innovations such as computational modeling and simulation allow for precise adjustments to management strategies by simulating internal greenhouse conditions, thereby improving energy efficiency and plant growth. Wireless sensor networks (WSN) can be implemented to monitor environmental parameters and automatically adjust the indoor environment to optimize growing conditions.
An analysis of economic and environmental benefits reveals substantial advantages. Adopting advanced climate control technologies and high-efficiency materials can result in significant long-term savings in operating costs. The use of automated systems and renewable energies, such as solar and geothermal energy, can significantly reduce dependence on fossil fuels and energy costs [224]. Studies show that the integration of heat recovery systems can increase energy efficiency by up to 40%, providing a return on investment in a few months [295]. Additionally, these technologies significantly contribute to reducing greenhouse gas emissions, mitigating the environmental impacts of intensive agriculture. The integration of semi-transparent photovoltaic systems not only provides clean energy but also reduces the need for electricity from non-renewable sources, promoting environmental sustainability [44].

7. Conclusions

Judicious location selection, innovative design and selection of greenhouse construction materials play a key role in maximizing energy efficiency. Strategic positioning of greenhouses can optimize the capture of natural sunlight while choosing materials with good insulation properties can minimize heat loss. This integrated approach, which considers both energy and agronomic needs, is crucial to creating a growing environment that promotes healthy plant growth with the lowest possible energy consumption.
The implementation of heating and cooling systems, both passive and active, is essential to maintain ideal conditions inside the greenhouse. Passive systems, such as natural ventilation, take advantage of environmental resources without consuming additional energy, while active systems, which may include the use of heaters and coolers, offer more precise control of indoor conditions. The use of innovative covering materials, which adjust light transmission, can further improve the indoor microclimate, resulting in energy savings and promoting optimal plant growth.
Emerging technologies, such as the use of photovoltaic solar energy and automation systems, represent a promising frontier for greenhouses. The integration of solar photovoltaics can provide a clean, renewable energy source, while advanced automation allows for precise monitoring and adjustment of indoor conditions, optimizing resource use and improving energy efficiency. These innovations offer opportunities to make agricultural production more sustainable and adapted to climate change.
The need for continued research and development is evident in the search for more efficient energy solutions for greenhouses. Interdisciplinary collaboration between agronomists, engineers and environmental scientists is essential to develop technologies and strategies that meet specific climatic conditions and crop needs. The development of customized solutions can lead to significant advances in greenhouse systems that combine energy efficiency with high agricultural productivity.
Improving the energy performance of greenhouses not only contributes to reducing the carbon footprint of food production but also plays a crucial role in global food security. By increasing the efficiency and sustainability of food production, greenhouses can offer a viable solution for feeding a growing population in a world affected by climate change. Continued innovation in agricultural greenhouses represents a key strategy to achieve more sustainable and resilient food production.

Author Contributions

Conceptualization, R.P.C., L.C.C.P. and P.D.d.S.; methodology, R.P.C.; formal analysis, L.C.C.P. and P.D.d.S.; investigation, R.P.C.; resources, R.P.C.; writing—original draft preparation, R.P.C.; writing—review and editing, P.D.d.S. and L.C.C.P.; visualization, P.D.d.S. and L.C.C.P.; supervision, L.C.C.P. and P.D.d.S.; project administration, P.D.d.S. and L.C.C.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported in part by the Fundação para a Ciência e Tecnologia (FCT) and C-MAST (Centre for Mechanical and Aerospace Science and Technologies), under project UIDB/00151/2020.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MDPIMultidisciplinary Digital Publishing Institute
DOAJDirectory of open access journals
TLAThree letter acronym
LDLinear dichroism
UNUnited Nations
EAHEEarth-Air Heat
MCDAMulti-criteria Decision Analysis
GISGeographic Information Systems
DEMDigital Elevation Data Modeling
F-DEMATELFUZZY-DEcision-MAking Trial and Evaluation Laboratory
PVPhotovoltaic
AHPAnalytical Hierarchy Process
BESBuilding Energy Simulation
OSCsOrganic Solar Cells
CPV/TPhotovoltaic/Thermal concentrator
TESThermal Energy Storage
PEPolyethylene
EVAEthylVinylAcetate
PCPolycarbonate
UVUltra Violet
PCMsPhase Transition Materials
PARPhotosynthetic Active Radiation
CBDCompletely Randomized Design
EUEuropean Union
MHPAsMicro Heat Pipe Arrays
CFDComputational Fluid Dynamic
CMVControlled Mechanical Ventilation
DSSCDye Sensitized Solar Cell
OPVOrganic PhotoVoltaic
ACHAir Changes Per Hour
FEMFinite Element Method
FVMFinite Volume Method
3DThree Dimensional
ANFISAdaptive Neuro-Fuzzy Inference System
LSISLaser Spray Irrigation System
UNMROne-sided Nuclear Magnetic Resonance
PPFDPhotosynthetic Photon Flux Density
FAMFuzzy Associative Memory
ANNArtificial Neural Networks
UAVsDrones and Unmanned Aerial Vehicles
RCNNRegional Convolutional Neural Networks
WSNWireless Sensor Networks

References

  1. UNICEF. Trends in Maternal Mortality 2000 to 2017 Estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division; World Health Organization: Geneva, Switzerland, 2019. [Google Scholar]
  2. Vollset, S.E.; Goren, E.; Yuan, C.W.; Cao, J.; Smith, A.E.; Hsiao, T.; Bisignano, C.; Azhar, G.S.; Castro, E.; Chalek, J.; et al. Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: A forecasting analysis for the Global Burden of Disease Study. Lancet 2020, 396, 1285–1306. [Google Scholar] [CrossRef] [PubMed]
  3. Gourdo, L.; Fatnassi, H.; Bouharroud, R.; Ezzaeri, K.; Bazgaou, A.; Wifaya, A.; Demrati, H.; Bekkaoui, A.; Aharoune, A.; Poncet, C.; et al. Heating Canarian greenhouse with a passive solar water–sleeve system: Effect on microclimate and tomato crop yield. Sol. Energy 2019, 188, 1349–1359. [Google Scholar] [CrossRef]
  4. Banakar, A.; Montazeri, M.; Ghobadian, B.; Pasdarshahri, H.; Kamrani, F. Energy analysis and assessing heating and cooling demands of closed greenhouse in Iran. Therm. Sci. Eng. Prog. 2021, 25, 101042. [Google Scholar] [CrossRef]
  5. Neugebauer, M.; Hałacz, J.; Olkowski, T. A compost heating solution for a greenhouse in north-eastern Poland in fall. J. Clean. Prod. 2021, 279, 123613. [Google Scholar] [CrossRef]
  6. Yano, A.; Cossu, M. Energy sustainable greenhouse crop cultivation using photovoltaic technologies. Renew. Sustain. Energy Rev. 2019, 109, 116–137. [Google Scholar] [CrossRef]
  7. Sahdev, R.K.; Kumar, M.; Dhingra, A.K. A comprehensive review of greenhouse shapes and its applications. Front. Energy 2019, 13, 427–438. [Google Scholar] [CrossRef]
  8. Dhiman, M.; Sethi, V.P.; Singh, B.; Sharma, A. CFD analysis of greenhouse heating using flue gas and hot water heat sink pipe networks. Comput. Electron. Agric. 2019, 163, 104853. [Google Scholar] [CrossRef]
  9. Xu, W.; Guo, H.; Ma, C. An active solar water wall for passive solar greenhouse heating. Appl. Energy 2022, 308, 118270. [Google Scholar] [CrossRef]
  10. Hamdane, S.; Pires, L.C.C.; Silva, P.D.; Gaspar, P.D. Evaluating the thermal performance and environmental impact of agricultural greenhouses using earth-to-air heat exchanger: An experimental study. Appl. Sci. 2023, 13, 1119. [Google Scholar] [CrossRef]
  11. Al-Helal, I.; Alsadon, A.; Marey, S.; Ibrahim, A.; Shady, M.; Abdel-Ghany, A. Geothermal energy potential for cooling/heating greenhouses in hot arid regions. Atmosphere 2022, 13, 105. [Google Scholar] [CrossRef]
  12. Al-Helal, I.; Picuno, P.; Alsadon, A.A.; Ibrahim, A.; Shady, M.; Abdel-Ghany, A.M. Effect of shape, orientation and aging of a plastic greenhouse cover on the degradation rate of the optical properties in arid climates. Appl. Sci. 2022, 12, 2709. [Google Scholar] [CrossRef]
  13. Chen, C.; Li, Y.; Li, N.; Wei, S.; Yang, F.; Ling, H.; Yu, N.; Han, F. A computational model to determine the optimal orientation for solar greenhouses located at different latitudes in China. Sol. Energy 2018, 165, 19–26. [Google Scholar] [CrossRef]
  14. Dragicevic, S. Determining the optimum orientation of a greenhouse on the basis of the total solar radiation availability. Therm. Sci. 2011, 15, 215–221. [Google Scholar] [CrossRef]
  15. Pieters, J.G.; Deltour, J.M. Modelling solar energy input in greenhouses. Sol. Energy 1999, 67, 119–130. [Google Scholar] [CrossRef]
  16. Cossu, M.; Murgia, L.; Ledda, L.; Deligios, P.A.; Sirigu, A.; Chessa, F.; Pazzona, A. Solar radiation distribution inside a greenhouse with south-oriented photovoltaic roofs and effects on crop productivity. Appl. Energy 2014, 133, 89–100. [Google Scholar] [CrossRef]
  17. Aguilar, M.A.; Bianconi, F.; Aguilar, F.J.; Fernández, I. Object-based greenhouse classification from GeoEye-1 and WorldView-2 stereo imagery. Remote Sens. 2014, 6, 3554–3582. [Google Scholar] [CrossRef]
  18. Dar, I.A.; Sankar, K.; Dar, M.A. Remote sensing technology and geographic information system modeling: An integrated approach towards the mapping of groundwater potential zones in Hardrock terrain, Mamundiyar basin. J. Hydrol. 2010, 394, 285–295. [Google Scholar] [CrossRef]
  19. Graeff, C.; Loui, M.C. Ethical implications of technical limitations in GIS. IEEE Technol. Soc. Mag. 2008, 27, 27–36. [Google Scholar] [CrossRef]
  20. Zambon, K.L.; Carneiro, A.A.F.M.; Silva, A.N.R.; Negri, J.C. Multicriteria Decision Analysis for Site Selection of Thermoelectric Power Plants Using GIS. Pesqui. Oper. 2005, 25, 183–199. [Google Scholar] [CrossRef]
  21. Martinez, C.; Hancock, G.R.; Kalma, J.D.; Wells, T.; Boland, L. An assessment of digital elevation models and their ability to capture geomorphic and hydrologic properties at the catchment scale. Int. J. Remote Sens. 2010, 31, 6239–6257. [Google Scholar] [CrossRef]
  22. Ajayi, O.G.; Salubi, A.A.; Angbas, A.F.; Odigure, M.G. Generation of accurate digital elevation models from UAV acquired low percentage overlapping images. Int. J. Remote Sens. 2017, 38, 3113–3134. [Google Scholar] [CrossRef]
  23. Liu, Y.; Li, D.; Wan, S.; Wang, F.; Dou, W.; Xu, X.; Li, S.; Ma, R.; Qi, L. A long short-term memory-based model for greenhouse climate prediction. Int. J. Intell. Syst. 2022, 37, 135–151. [Google Scholar] [CrossRef]
  24. Kearney, M.R.; Shamakhy, A.; Tingley, R.; Karoly, D.J.; Hoffmann, A.A.; Briggs, P.R.; Porter, W.P. Microclimate modelling at macro scales: A test of a general microclimate model integrated with gridded continental-scale soil and weather data. Methods Ecol. Evol. 2014, 5, 273–286. [Google Scholar] [CrossRef]
  25. Esmaeli, H.; Roshandel, R. Optimal design for solar greenhouses based on climate conditions. Renew. Energy 2020, 145, 1255–1265. [Google Scholar] [CrossRef]
  26. Mohammadi, B.; Ranjbar, S.F.; Ajabshirchi, Y. Application of dynamic model to predict some inside environment variables in a semi-solar greenhouse. Inf. Process. Agric. 2018, 5, 279–288. [Google Scholar] [CrossRef]
  27. El-Maghlany, W.M.; Teamah, M.A.; Tanaka, H. Optimum design and orientation of the greenhouses for maximum capture of solar energy in North Tropical Region. Energy Convers. Manag. 2015, 105, 1096–1104. [Google Scholar] [CrossRef]
  28. Chen, J.; Ma, Y.; Pang, Z. A mathematical model of global solar radiation to select the optimal shape and orientation of the greenhouses in southern China. Sol. Energy 2020, 205, 380–389. [Google Scholar] [CrossRef]
  29. Badji, A.; Benseddik, A.; Bensaha, H.; Boukhelifa, A.; Hasrane, I. Design, technology, and management of greenhouse: A review. J. Clean. Prod. 2022, 373, 133753. [Google Scholar] [CrossRef]
  30. Stanciu, C.; Stanciu, D.; Dobrovicescu, A. Effect of greenhouse orientation with respect to EW axis on its required heating and cooling loads. Energy Procedia 2016, 85, 498–504. [Google Scholar] [CrossRef]
  31. Aissa, M.; Bezari, S. The orientation effect of the agricultural tunnel greenhouse on aerodynamic and energy properties. In Proceedings of the 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA), Rome, Italy, 24–26 September 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–4. [Google Scholar]
  32. Mercan, Y.; Sezgin, F. The Use Of Multi-Criteria Decision Analysis (MCDA) And Geographic Information System (GIS) In Selection Of Greenhouse Site Location: The Case Of Aydin Region In Türkiye. Adnan Menderes Univ. Ziraat Fak. Derg. 2023, 20, 149–158. [Google Scholar] [CrossRef]
  33. Vanthoor, B.; Stanghellini, C.; Van Henten, E.J.; de Visser, P. The effect of outdoor climate conditions on passive greenhouse design. Acta Hortic. 2009, 807, 61–66. [Google Scholar] [CrossRef]
  34. Jeong, J.S.; Ramírez-Gómez, Á. Optimizing the location of a biomass plant with a fuzzy-DEcision-MAking Trial and Evaluation Laboratory (F-DEMATEL) and multi-criteria spatial decision assessment for renewable energy management and long-term sustainability. J. Clean. Prod. 2018, 182, 509–520. [Google Scholar] [CrossRef]
  35. Feyzi, S.; Khanmohammadi, M.; Abedinzadeh, N.; Aalipour, M. Multi-criteria decision analysis FANP based on GIS for siting municipal solid waste incineration power plant in the north of Iran. Sustain. Cities Soc. 2019, 47, 101513. [Google Scholar] [CrossRef]
  36. Geetha, S.; Narayanamoorthy, S.; Kang, D.; Baleanu, D. An adoptive renewable energy resource selection using Hesitant Pythagorean Fuzzy DEMATEL and VIKOR methods. J. Intell. Fuzzy Syst. 2022, 43, 4285–4302. [Google Scholar] [CrossRef]
  37. Castilla, N. Greenhouse Technology and Management; CABI: Wallingford, UK, 2013. [Google Scholar]
  38. Odesola, I.F.; Ezekwem, C. The effect of shape and orientation on a greenhouse: A review. AFRREV STECH: Int. J. Sci. Technol. 2012, 1, 122–130. [Google Scholar]
  39. Gupta, R.; Tiwari, G.N.; Kumar, A.; Gupta, Y. Calculation of total solar fraction for different orientation of greenhouse using 3D-shadow analysis in Auto-CAD. Energy Build. 2012, 47, 27–34. [Google Scholar] [CrossRef]
  40. Ahamed, M.S.; Guo, H.; Tanino, K. Energy saving techniques for reducing the heating cost of conventional greenhouses. Biosyst. Eng. 2019, 178, 9–33. [Google Scholar] [CrossRef]
  41. Saltuk, B.; Artun, O. Multi-criteria decision system for greenhouse site selection in Lower Euphrates Basin using geographic information systems (GIS). Afr. J. Agric. Res. 2018, 13, 2716–2724. [Google Scholar]
  42. Hernandez, C.; del Sagrado, J.; Rodriguez, F.; Moreno, J.C.; Sánchez, J.A. Modeling of energy demand of a high-tech greenhouse in warm climate based on bayesian networks. Math. Probl. Eng. 2015, 2015, 1–10. [Google Scholar] [CrossRef]
  43. Rasheed, A.; Lee, J.W.; Lee, H.W. Development and optimization of a building energy simulation model to study the effect of greenhouse design parameters. Energies 2018, 11, 2001. [Google Scholar] [CrossRef]
  44. Ravishankar, E.; Booth, R.E.; Saravitz, C.; Sederoff, H.; Ade, H.W.; O’Connor, B.T. Achieving net zero energy greenhouses by integrating semitransparent organic solar cells. Joule 2020, 4, 490–506. [Google Scholar] [CrossRef]
  45. Lebre, B.; Silva, P.D.; Pires, L.C.; Gaspar, P.D. Computational Modeling of the Thermal Behavior of a Greenhouse. Appl. Sci. 2021, 11, 11816. [Google Scholar] [CrossRef]
  46. Yang, F.; Fan, Y.; Wu, X.; Cheng, Y.; Liu, Q.; Feng, L.; Chen, J.; Wang, Z.; Wang, X.; Yong, T.; et al. Auxin-to-gibberellin ratio as a signal for light intensity and quality in regulating soybean growth and matter partitioning. Front. Plant Sci. 2018, 9, 56. [Google Scholar] [CrossRef] [PubMed]
  47. Feng, L.; Raza, M.A.; Li, Z.; Chen, Y.; Khalid, M.H.B.; Du, J.; Liu, W.; Wu, X.; Song, C.; Yu, L.; et al. The influence of light intensity and leaf movement on photosynthesis characteristics and carbon balance of soybean. Front. Plant Sci. 2019, 9, 1952. [Google Scholar] [CrossRef] [PubMed]
  48. Teixeira-Gandra, C.F.A.; Damé, R.C.F.; Simonete, M.A. Predição da precipitação a partir das coordenadas geográficas no Estado do Rio Grande do Sul. Rev. Bras. Geogr. Fis. 2015, 8, 848–856. [Google Scholar] [CrossRef]
  49. Häder, D.P.; Cabrol, N.A. Monitoring of solar irradiance in the high Andes. Photochem. Photobiol. 2020, 96, 1133–1139. [Google Scholar] [CrossRef] [PubMed]
  50. Huang, L.; Deng, L.; Li, A.; Gao, R.; Zhang, L.; Lei, W. Analytical model for solar radiation transmitting the curved transparent surface of solar greenhouse. J. Build. Eng. 2020, 32, 101785. [Google Scholar] [CrossRef]
  51. Moshari, A.; Aslani, A.; Entezari, A.; Ghanbari, K. Performance assessment of the integration of semitransparent solar cells with different geometry of greenhouses under different climate regions. Environ. Sci. Pollut. Res. 2023, 30, 62281–62294. [Google Scholar] [CrossRef]
  52. Maraveas, C.; Karavas, C.S.; Loukatos, D.; Bartzanas, T.; Arvanitis, K.G.; Symeonaki, E. Agricultural greenhouses: Resource management technologies and perspectives for zero greenhouse gas emissions. Agriculture 2023, 13, 1464. [Google Scholar] [CrossRef]
  53. Zhang, X.; Lv, J.; Xie, J.; Yu, J.; Zhang, J.; Tang, C.; Li, J.; He, Z.; Wang, C. Solar radiation allocation and spatial distribution in Chinese solar greenhouses: Model development and application. Energies 2020, 13, 1108. [Google Scholar] [CrossRef]
  54. Soares, G.F.W.; Vieira, L.S.R.; Galdino, M.A.E.; de Almeida Oliveiri, M.M.; de Paula Borges, E.L.; de Carvalho, C.M.; Lima, A.A.N. Comparação de custos entre sistemas fotovoltaicos individuais e minicentrais fotovoltaicas para eletrificação rural. In Proceedings of the Congresso Brasileiro de Energia Solar-CBENS, Fortaleza, Brazil, 3–6 November 2010; pp. 1–6. [Google Scholar]
  55. Polman, A.; Knight, M.; Garnett, E.C.; Ehrler, B.; Sinke, W.C. Photovoltaic materials: Present efficiencies and future challenges. Science 2016, 352, aad4424. [Google Scholar] [CrossRef] [PubMed]
  56. Furini, P.H.; Makiyama, M.K.; Orso, K.D.F.; Casonatto, D.C. Efeitos do sombreamento em módulos fotovoltaicos half-cell de 144 células. Anais Eng. Mec. 2022, 6, 163–184. [Google Scholar]
  57. Simões, M.C.S.; Brito, N.S.D.; da Silva, C.A. Análise dos Impactos da Conexão de Usinas Fotovoltaicas na Estabilidade de Tensão do Sistema Elétrico. Simpósio Bras. Sist. Elétr. SBSE 2020, 1, 1. [Google Scholar]
  58. Sonneveld, P.J.; Swinkels, G.L.A.M.; Van Tuijl, B.A.J.; Janssen, H.J.J.; Campen, J.; Bot, G.P.A. Performance of a concentrated photovoltaic energy system with static linear Fresnel lenses. Sol. Energy 2011, 85, 432–442. [Google Scholar] [CrossRef]
  59. Wu, G.; Yang, Q.; Zhang, Y.; Fang, H.; Feng, C.; Zheng, H. Energy and optical analysis of photovoltaic thermal integrated with rotary linear curved Fresnel lens inside a Chinese solar greenhouse. Energy 2020, 197, 117215. [Google Scholar] [CrossRef]
  60. Li, Z.; Ma, X.; Zhao, Y.; Zheng, H. Study on the performance of a curved Fresnel solar concentrated system with seasonal underground heat storage for the greenhouse application. J. Sol. Energy Eng. 2019, 141, 011004. [Google Scholar] [CrossRef]
  61. Tripanagnostopoulos, Y.; Siabekou, C.; Tonui, J.K. The Fresnel lens concept for solar control of buildings. Sol. Energy 2007, 81, 661–675. [Google Scholar] [CrossRef]
  62. Tripanagnostopoulos, Y.; Souliotis, M.; Tonui, J.K.; Kavga, A. Irradiation aspects for energy balance in greenhouses. In Proceedings of the International Conference on Sustainable Greenhouse Systems-Greensys2004, Leuven, Belgium, 12–16 September 2004; ISHS: Leuven, Belgium, 2004; pp. 733–740. [Google Scholar]
  63. Miller, D.C.; Kurtz, S.R. Durability of Fresnel lenses: A review specific to the concentrating photovoltaic application. Sol. Energy Mater. Sol. Cells 2011, 95, 2037–2068. [Google Scholar] [CrossRef]
  64. Gorjian, S.; Calise, F.; Kant, K.; Ahamed, M.S.; Copertaro, B.; Najafi, G.; Zhang, X.; Aghaei, M.; Shamshiri, R.R. A review on opportunities for implementation of solar energy technologies in agricultural greenhouses. J. Clean. Prod. 2021, 285, 124807. [Google Scholar] [CrossRef]
  65. Ghani, S.; Bakochristou, F.; ElBialy, E.M.A.A.; Gamaledin, S.M.A.; Rashwan, M.M.; Abdelhalim, A.M.; Ismail, S.M. Design challenges of agricultural greenhouses in hot and arid environments: A review. Eng. Agric. Environ. Food 2019, 12, 48–70. [Google Scholar] [CrossRef]
  66. Mazzeo, D.; Baglivo, C.; Panico, S.; Congedo, P.M. Solar greenhouses: Climates, glass selection, and plant well-being. Sol. Energy 2021, 230, 222–241. [Google Scholar] [CrossRef]
  67. Singh, R.D.; Tiwari, G.N. Energy conservation in the greenhouse system: A steady state analysis. Energy 2010, 35, 2367–2373. [Google Scholar] [CrossRef]
  68. Pakari, A.; Ghani, S. Evaluation of a novel greenhouse design for reduced cooling loads during the hot season in subtropical regions. Sol. Energy 2019, 181, 234–242. [Google Scholar] [CrossRef]
  69. Sethi, V.P. On the selection of shape and orientation of a greenhouse: Thermal modeling and experimental validation. Sol. Energy 2009, 83, 21–38. [Google Scholar] [CrossRef]
  70. Claudino, P. Experimental and Modelling Study of a Geodesic Dome Solar Greenhouse System in Ottawa. Ph.D. Thesis, Carleton University, Ottawa, ON, Canada, 2016. [Google Scholar]
  71. Cossu, M.; Cossu, A.; Deligios, P.A.; Ledda, L.; Li, Z.; Fatnassi, H.; Poncet, C.; Yano, A. Assessment and comparison of the solar radiation distribution inside the main commercial photovoltaic greenhouse types in Europe. Renew. Sustain. Energy Rev. 2018, 94, 822–834. [Google Scholar] [CrossRef]
  72. Mellalou, A.; Mouaky, A.; Bacaoui, A.; Outzourhit, A. A comparative study of greenhouse shapes and orientations under the climatic conditions of Marrakech, Morocco. Int. J. Environ. Sci. Technol. 2021, 18, 6045–6056. [Google Scholar] [CrossRef]
  73. Akrami, M.; Javadi, A.A.; Hassanein, M.J.; Farmani, R.; Dibaj, M.; Tabor, G.R.; Negm, A. Study of the effects of vent configuration on mono-span greenhouse ventilation using computational fluid dynamics. Sustainability 2020, 12, 986. [Google Scholar] [CrossRef]
  74. Liu, J.; Wu, X.; Sun, F.; Wang, B. Development and Application of a Crossed Multi-Arch Greenhouse in Tropical China. Agriculture 2022, 12, 2164. [Google Scholar] [CrossRef]
  75. Serale, G.; Gnoli, L.; Giraudo, E.; Fabrizio, E. A supervisory control strategy for improving energy efficiency of artificial lighting systems in greenhouses. Energies 2021, 14, 202. [Google Scholar] [CrossRef]
  76. Fan, Z.; Li, Y.; Jiang, L.; Wang, L.; Li, T.; Liu, X. Analysis of the Effect of Exhaust Configuration and Shape Parameters of Ventilation Windows on Microclimate in Round Arch Solar Greenhouse. Sustainability 2023, 15, 6432. [Google Scholar] [CrossRef]
  77. Li, H.; Li, Y.; Yue, X.; Liu, X.; Tian, S.; Li, T. Evaluation of airflow pattern and thermal behavior of the arched greenhouses with designed roof ventilation scenarios using CFD simulation. PLoS ONE 2020, 15, e0239851. [Google Scholar] [CrossRef] [PubMed]
  78. Goswami, D.Y.; Lavania, A.; Shahbazi, S.; Masood, M. Analysis of a geodesic dome solar fruit dryer. Dry. Technol. 1991, 9, 677–691. [Google Scholar] [CrossRef]
  79. Boulard, T.; Wang, S. Experimental and numerical studies on the heterogeneity of crop transpiration in a plastic tunnel. Comput. Electron. Agric. 2002, 34, 173–190. [Google Scholar] [CrossRef]
  80. Condori, M.; Echazu, R.; Saravia, L. Solar drying of sweet pepper and garlic using the tunnel greenhouse drier. Renew. Energy 2001, 22, 447–460. [Google Scholar] [CrossRef]
  81. Marucci, A.; Zambon, I.; Colantoni, A.; Monarca, D. A combination of agricultural and energy purposes: Evaluation of a prototype of photovoltaic greenhouse tunnel. Renew. Sustain. Energy Rev. 2018, 82, 1178–1186. [Google Scholar] [CrossRef]
  82. Ahamed, M.S.; Guo, H.; Tanino, K. Energy-efficient design of greenhouse for Canadian Prairies using a heating simulation model. Int. J. Energy Res. 2018, 42, 2263–2272. [Google Scholar] [CrossRef]
  83. Bibi-Triki, N.; Bendimerad, S.; Chermiti, A.; Mahdjoub, T.; Draoui, B.; Abène, A. Modeling, characterization and analysis of the dynamic behavior of heat transfers through polyethylene and glass walls of greenhouses. Phys. Procedia 2011, 21, 67–74. [Google Scholar] [CrossRef]
  84. Robles Algarín, C.; Callejas Cabarcas, J.; Polo Llanos, A. Low-cost fuzzy logic control for greenhouse environments with web monitoring. Electronics 2017, 6, 71. [Google Scholar] [CrossRef]
  85. Choab, N.; Allouhi, A.; El Maakoul, A.; Kousksou, T.; Saadeddine, S.; Jamil, A. Effect of greenhouse design parameters on the heating and cooling requirement of greenhouses in Moroccan climatic conditions. IEEE Access 2020, 9, 2986–3003. [Google Scholar] [CrossRef]
  86. Karambasti, B.M.; Naghashzadegan, M.; Ghodrat, M.; Ghorbani, G.; Simorangkir, R.B.V.B.; Lalbakhsh, A. Optimal solar greenhouses design using multiobjective genetic algorithm. IEEE Access 2022, 10, 73728–73742. [Google Scholar] [CrossRef]
  87. Mobtaker, H.G.; Ajabshirchi, Y.; Ranjbar, S.F.; Matloobi, M. Solar energy conservation in greenhouse: Thermal analysis and experimental validation. Renew. Energy 2016, 96, 509–519. [Google Scholar] [CrossRef]
  88. Mobtaker, H.G.; Ajabshirchi, Y.; Ranjbar, S.F.; Matloobi, M. Simulation of thermal performance of solar greenhouse in north-west of Iran: An experimental validation. Renew. Energy 2019, 135, 88–97. [Google Scholar] [CrossRef]
  89. Lamnatou, C.; Chemisana, D. Solar radiation manipulations and their role in greenhouse claddings: Fresnel lenses, NIR-and UV-blocking materials. Renew. Sustain. Energy Rev. 2013, 18, 271–287. [Google Scholar] [CrossRef]
  90. Maraveas, C.; Kotzabasaki, M.I.; Bayer, I.S.; Bartzanas, T. Sustainable Greenhouse Covering Materials with Nano-and Micro-Particle Additives for Enhanced Radiometric and Thermal Properties and Performance. AgriEngineering 2023, 5, 1347–1377. [Google Scholar] [CrossRef]
  91. Zhang, S.; Chen, Z.; Cao, C.; Gao, Y. Near-Infrared Reflective Greenhouse Covering: A Novel Strategy for Electricity-Free Cooling. ACS Agric. Sci. Technol. 2024, in press. [CrossRef]
  92. Baeza, E.; López, J.C. Light transmission through greenhouse covers. In Proceedings of the VII International Symposium on Light in Horticultural Systems, Wageningen, The Netherlands, 15–19 October 2012; ISHS: Leuven, Belgium, 2012; pp. 425–440. [Google Scholar]
  93. Feng, C.; Yuan, G.; Wang, R.; Chen, X.; Ma, F.; Yang, H.; Li, X. Performance study on a novel greenhouse cover structure with beam split and heat control function. Energy Convers. Manag. 2024, 301, 118077. [Google Scholar] [CrossRef]
  94. Katsoulas, N.; Bari, A.; Papaioannou, C. Plant responses to UV blocking greenhouse covering materials: A review. Agronomy 2020, 10, 1021. [Google Scholar] [CrossRef]
  95. Maraveas, C. Environmental sustainability of greenhouse covering materials. Sustainability 2019, 11, 6129. [Google Scholar] [CrossRef]
  96. Xu, H.; Ding, J.; Li, T.; Mu, C.; Gu, X.; Wang, R. A study on optimum insulation thickness in walls of Chinese solar greenhouse for energy saving. Agronomy 2022, 12, 1104. [Google Scholar] [CrossRef]
  97. Dong, Y.; Kong, J.; Mousavi, S.; Rismanchi, B.; Yap, P.S. Wall insulation materials in different climate zones: A review on challenges and opportunities of available alternatives. Thermo 2023, 3, 38–65. [Google Scholar] [CrossRef]
  98. Swinkels, G.L.A.M.; Sonneveld, P.J.; Bot, G.P.A. SE—Structures and environment: Improvement of greenhouse insulation with restricted transmission loss through zigzag covering material. J. Agric. Eng. Res. 2001, 79, 91–97. [Google Scholar] [CrossRef]
  99. Du, W.-C.; Xie, J.; Xia, L.; Liu, Y.-J.; Yang, H.-W.; Zhang, Y. Study of new solar film based on near-infrared shielding. J. Photochem. Photobiol. A Chem. 2021, 418, 113410. [Google Scholar] [CrossRef]
  100. Briassoulis, D.; Waaijenberg, D.; Gratraud, J.; Von Eslner, B. Mechanical properties of covering materials for greenhouses: Part 1, general overview. J. Agric. Eng. Res. 1997, 67, 81–96. [Google Scholar] [CrossRef]
  101. Waaijenberg, D. Design, construction and maintenance of greenhouse structures. In Proceedings of the International Symposium on Greenhouses, Environmental Controls and In-house Mechanization for Crop Production in the Tropics, Leiden, The Netherlands, 6–10 September 2004; ISHS: Leuven, Belgium, 2004; pp. 31–42. [Google Scholar]
  102. Al-Mahdouri, A.; Baneshi, M.; Gonome, H.; Okajima, J.; Maruyama, S. Evaluation of optical properties and thermal performances of different greenhouse covering materials. Sol. Energy 2013, 96, 21–32. [Google Scholar] [CrossRef]
  103. Zhang, Y.; Gauthier, L.; de Halleux, D.; Dansereau, B.; Gosselin, A. Effect of covering materials on energy consumption and greenhouse microclimate. Agric. For. Meteorol. 1996, 82, 227–244. [Google Scholar] [CrossRef]
  104. Paneri, A.; Wong, I.L.; Burek, S. Transparent insulation materials: An overview on past, present and future developments. Sol. Energy 2019, 184, 59–83. [Google Scholar] [CrossRef]
  105. Wang, H.; Li, J.; Cheng, M.; Zhang, F.; Wang, X.; Fan, J.; Wu, L.; Fang, D.; Zou, H.; Xiang, Y. Optimal drip fertigation management improves yield, quality, water and nitrogen use efficiency of greenhouse cucumber. Sci. Hortic. 2019, 243, 357–366. [Google Scholar] [CrossRef]
  106. Jha, M.K.; Paikra, S.S.; Sahu, M.R. Protected Cultivation of Horticulture Crops; Educreation Publishing: Bilaspur, India, 2019. [Google Scholar]
  107. Li, Y.; Sun, D.; Xia, T.; Varbanov, P.S.; Liu, X.; Li, T. Performance of a novel internal insulation in Chinese solar greenhouse for the cleaner and energy-saving production in high latitudes and cold regions. J. Clean. Prod. 2023, 412, 137442. [Google Scholar] [CrossRef]
  108. Mavroeidis, A.; Bilalis, D.; Tataridas, A.; Roussis, I.; Kakabouki, I.; Folina, A.; Papastyliannou, P.; Stefanopoulos, A. Do Greenhouse Cover Materials Affect Cannabis Performance? Bull. Univ. Agric. Sci. Vet. Med. Cluj-Napoca Hortic. 2021, 78, 117–122. [Google Scholar] [CrossRef]
  109. Papadakis, G.; Briassoulis, D.; Mugnozza, G.S.; Vox, G.; Feuilloley, P.; Stoffers, J.A. Review Paper (SE—Structures and Environment): Radiometric and thermal properties of, and testing methods for, greenhouse covering materials. J. Agric. Eng. Res. 2000, 77, 7–38. [Google Scholar] [CrossRef]
  110. Kim, H.-K.; Kang, G.-C.; Moon, J.-P.; Lee, T.-S.; Oh, S.-S. Estimation of thermal performance and heat loss in plastic greenhouses with and without thermal curtains. Energies 2018, 11, 578. [Google Scholar] [CrossRef]
  111. Kavga, A.; Souliotis, M.; Koumoulos, E.P.; Fokaides, P.A.; Charitidis, C.A. Environmental and nanomechanical testing of an alternative polymer nanocomposite greenhouse covering material. Sol. Energy 2018, 159, 1–9. [Google Scholar] [CrossRef]
  112. Stirbet, A.; Lazár, D.; Guo, Y.; Govindjee, G. Photosynthesis: Basics, history and modelling. Ann. Bot. 2020, 126, 511–537. [Google Scholar] [CrossRef] [PubMed]
  113. Techawongstien, S. Factors affecting plant growth and development. Khon Kaen J. Sci. Technol. 2016, 24, 45–67. [Google Scholar]
  114. Hatfield, J.L.; Prueger, J.H. Temperature extremes: Effect on plant growth and development. Weather Clim. Extrem. 2015, 10, 4–10. [Google Scholar] [CrossRef]
  115. Salazar, R.; Rojano, A.; López, I.; Schmidt, U. A Model for the Combine Description of the Temperature and Relative Humidity Regime in the Greenhouse. In Proceedings of the 2010 Ninth Mexican International Conference on Artificial Intelligence, Pachuca, Mexico, 8–13 November 2010; IEEE: Piscataway, NJ, USA, 2010; pp. 113–117. [Google Scholar]
  116. Körner, O.; Challa, H. Process-based humidity control regime for greenhouse crops. Comput. Electron. Agric. 2003, 39, 173–192. [Google Scholar] [CrossRef]
  117. Kramer, P.J.; Boyer, J.S. Water Relations of Plants and Soils; Academic Press: San Diego, CA, USA, 1995. [Google Scholar]
  118. Jones, H.G. Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology, 3rd ed.; Cambridge University Press: Cambridge, UK, 2013. [Google Scholar]
  119. Taiz, L.; Zeiger, E.; Møller, I.M.; Murphy, A. Plant Physiology and Development, 6th ed.; Sinauer Associates Incorporated: Sunderland, MA, USA, 2015. [Google Scholar]
  120. Monteith, J.; Unsworth, M. Principles of Environmental Physics: Plants, Animals, and the Atmosphere, 4th ed.; Academic Press: Cambridge, MA, USA, 2013. [Google Scholar]
  121. Danneberger, T.K. Effects of humidity on plant growth. In Plant-Environment Interactions; CRC Press: Boca Raton, FL, USA, 2000; pp. 361–378. [Google Scholar]
  122. Soussi, M.; Chaibi, M.T.; Buchholz, M.; Saghrouni, Z. Comprehensive review on climate control and cooling systems in greenhouses under hot and arid conditions. Agronomy 2022, 12, 626. [Google Scholar] [CrossRef]
  123. Liu, X.; Li, H.; Li, Y.; Yue, X.; Tian, S.; Li, T. Effect of internal surface structure of the north wall on Chinese solar greenhouse thermal microclimate based on computational fluid dynamics. PLoS ONE 2020, 15, e0231316. [Google Scholar] [CrossRef] [PubMed]
  124. Li, Y.; Yue, X.; Zhao, L.; Xu, H.; Liu, X.; Li, T. Effect of north wall internal surface structure on heat storage-release performance and thermal environment of Chinese solar greenhouse. J. Build. Phys. 2022, 45, 507–527. [Google Scholar] [CrossRef]
  125. Zhang, X.; Lv, J.; Dawuda, M.M.; Xie, J.; Yu, J.; Gan, Y.; Zhang, J.; Tang, Z.; Li, J. Innovative passive heat-storage walls improve thermal performance and energy efficiency in Chinese solar greenhouses for non-arable lands. Sol. Energy 2019, 190, 561–575. [Google Scholar] [CrossRef]
  126. Chen, C.; Ling, H.; Zhai, Z.J.; Li, Y.; Yang, F.; Han, F.; Wei, S. Thermal performance of an active-passive ventilation wall with phase change material in solar greenhouses. Appl. Energy 2018, 216, 602–612. [Google Scholar] [CrossRef]
  127. Santolini, E.; Pulvirenti, B.; Guidorzi, P.; Bovo, M.; Torreggiani, D.; Tassinari, P. Analysis of the effects of shading screens on the microclimate of greenhouses and glass facade buildings. Build. Environ. 2022, 211, 108691. [Google Scholar] [CrossRef]
  128. Xia, T.; Li, Y.; Sun, Z.; Wan, X.; Sun, D.; Wang, L.; Liu, X.; Li, T. Performance study of an active solar water curtain heating system for Chinese solar greenhouse heating in high latitudes regions. Appl. Energy 2023, 332, 120548. [Google Scholar] [CrossRef]
  129. Downs, R.J. Environment and the Experimental Control of Plant Growth; Elsevier: Amsterdam, The Netherlands, 2012; Volume 6. [Google Scholar]
  130. Ferrante, A.; Mariani, L. Agronomic management for enhancing plant tolerance to abiotic stresses: High and low values of temperature, light intensity, and relative humidity. Horticulturae 2018, 4, 21. [Google Scholar] [CrossRef]
  131. Amoatey, P.; Al-Jabri, K.; Al-Saadi, S. Influence of phase change materials on thermal comfort, greenhouse gas emissions, and potential indoor air quality issues across different climatic regions: A critical review. Int. J. Energy Res. 2022, 46, 22386–22420. [Google Scholar] [CrossRef]
  132. Roslan, N.; Ya’acob, M.E.; Radzi, M.A.M.; Hashimoto, Y.; Jamaludin, D.; Chen, G. Dye Sensitized Solar Cell (DSSC) greenhouse shading: New insights for solar radiation manipulation. Renew. Sustain. Energy Rev. 2018, 92, 171–186. [Google Scholar] [CrossRef]
  133. Maraveas, C.; Loukatos, D.; Bartzanas, T.; Arvanitis, K.G.; Uijterwaal, J.F. Smart and solar greenhouse covers: Recent developments and future perspectives. Front. Energy Res. 2021, 9, 783587. [Google Scholar] [CrossRef]
  134. López-Díaz, G.; Carreño-Ortega, A.; Fatnassi, H.; Poncet, C.; Díaz-Pérez, M. The effect of different levels of shading in a photovoltaic greenhouse with a north–south orientation. Appl. Sci. 2020, 10, 882. [Google Scholar] [CrossRef]
  135. Moretti, S.; Marucci, A. A photovoltaic greenhouse with variable shading for the optimization of agricultural and energy production. Energies 2019, 12, 2589. [Google Scholar] [CrossRef]
  136. La Notte, L.; Giordano, L.; Calabrò, E.; Bedini, R.; Colla, G.; Puglisi, G.; Reale, A. Hybrid and organic photovoltaics for greenhouse applications. Appl. Energy 2020, 278, 115582. [Google Scholar] [CrossRef]
  137. Abdel-Ghany, A.M.; Picuno, P.; Al-Helal, I.; Alsadon, A.; Ibrahim, A.; Shady, M. Radiometric characterization, solar and thermal radiation in a greenhouse as affected by shading configuration in an arid climate. Energies 2015, 8, 13928–13937. [Google Scholar] [CrossRef]
  138. Aberkani, K.; Hao, X.; de Halleux, D.; Dorais, M.; Vineberg, S.; Gosselin, A. Effects of shading using a retractable liquid foam technology on greenhouse and plant microclimates. HortTechnology 2010, 20, 283–291. [Google Scholar] [CrossRef]
  139. Blanchard, M.G.; Runkle, E.S. Influence of NIR-reflecting shading paint on greenhouse environment, plant temperature, and growth and flowering of bedding plants. Trans. ASABE 2010, 53, 939–944. [Google Scholar] [CrossRef]
  140. Xu, Y.; Lyu, X.; Song, H.; Lin, B.; Wei, M.; Yin, Y.; Wang, S. Large-span M-shaped greenhouse with superior wind resistance and ventilation performance. J. Wind Eng. Ind. Aerodyn. 2023, 238, 105410. [Google Scholar] [CrossRef]
  141. Van den Bulck, N.; Coomans, M.; Wittemans, L.; Hanssens, J.; Steppe, K. Monitoring and energetic performance analysis of an innovative ventilation concept in a Belgian greenhouse. Energy Build. 2013, 57, 51–57. [Google Scholar] [CrossRef]
  142. Ghoulem, M.; El Moueddeb, K.; Nehdi, E.; Boukhanouf, R.; Calautit, J.K. Greenhouse design and cooling technologies for sustainable food cultivation in hot climates: Review of current practice and future status. Biosyst. Eng. 2019, 183, 121–150. [Google Scholar] [CrossRef]
  143. Akrami, M.; Salah, A.H.; Javadi, A.A.; Fath, H.E.S.; Hassanein, M.J.; Farmani, R.; Dibaj, M.; Negm, A. Towards a sustainable greenhouse: Review of trends and emerging practices in analysing greenhouse ventilation requirements to sustain maximum agricultural yield. Sustainability 2020, 12, 2794. [Google Scholar] [CrossRef]
  144. Ponce, P.; Molina, A.; Cepeda, P.; Lugo, E.; MacCleery, B. Greenhouse Design and Control; CRC Press: Boca Raton, FL, USA, 2014. [Google Scholar]
  145. Zhang, J.; Zhao, S.; Dai, A.; Wang, P.; Liu, Z.; Liang, B.; Ding, T. Greenhouse Natural Ventilation Models: How Do We Develop with Chinese Greenhouses? Agronomy 2022, 12, 1995. [Google Scholar] [CrossRef]
  146. Li, H.; Li, A.; Hou, Y.; Zhang, C.; Guo, J.; Li, J.; Ma, Y.; Wang, T.; Yin, Y. Analysis of Heat and Humidity in Single-Slope Greenhouses with Natural Ventilation. Buildings 2023, 13, 606. [Google Scholar] [CrossRef]
  147. Asthor. Greenhouse—ASTHOR—Agricultural/Commercial/Production. Available online: https://www.agriexpo.online/prod/asthor/product-175975-126081.html (accessed on 29 January 2024).
  148. Choi, Y.; Kang, N.; Park, K.; Chun, H.; Cho, M.; Lee, S.; Um, Y. Effect of greenhouse orientation on the environment of greenhouse and the growth and yield of tomato and oriental melon. Korean J. Hortic. Sci. Technol. 2008, 26, 380–386. [Google Scholar]
  149. Boulard, T.; Meneses, J.F.; Mermier, M.; Papadakis, G. The mechanisms involved in the natural ventilation of greenhouses. Agric. For. Meteorol. 1996, 79, 61–77. [Google Scholar] [CrossRef]
  150. Majdoubi, H.; Boulard, T.; Hanafi, A.; Bekkaoui, A.; Fatnassi, H.; Demrati, H.; Nya, M.; Bouirden, L. Natural ventilation performance of a large greenhouse equipped with insect screens. Trans. ASABE 2007, 50, 641–650. [Google Scholar] [CrossRef]
  151. Bournet, P.E.; Boulard, T. Effect of ventilator configuration on the distributed climate of greenhouses: A review of experimental and CFD studies. Comput. Electron. Agric. 2010, 74, 195–217. [Google Scholar] [CrossRef]
  152. Ali, A.; Iqbal, T.; Cheema, M.J.M.; Afzal, A.; Yasin, M.; Haq, Z.U.; Malik, A.M.; Khan, K.S. Development of a Low-Cost Biomass Furnace for Greenhouse Heating. Sustainability 2021, 13, 5152. [Google Scholar] [CrossRef]
  153. Roy, Y.; Lefsrud, M.; Orsat, V.; Filion, F.; Bouchard, J.; Nguyen, Q.; Dion, L.M.; Glover, A.; Madadian, E.; Lee, C.P. Biomass combustion for greenhouse carbon dioxide enrichment. Biomass Bioenergy 2014, 66, 186–196. [Google Scholar] [CrossRef]
  154. Van Henten, E.J. Automation and robotics in greenhouses. In Achieving Sustainable Greenhouse Cultivation; Burleigh Dodds Science Publishing: Cambridge, UK, 2019; pp. 359–378. [Google Scholar]
  155. Attar, I.; Farhat, A. Efficiency evaluation of a solar water heating system applied to the greenhouse climate. Sol. Energy 2015, 119, 212–224. [Google Scholar] [CrossRef]
  156. Bonuso, S.; Panico, S.; Baglivo, C.; Mazzeo, D.; Matera, N.; Congedo, P.M.; Oliveti, G. Dynamic analysis of the natural and mechanical ventilation of a solar greenhouse by coupling controlled mechanical ventilation (CMV) with an earth-to-air heat exchanger (EAHX). Energies 2020, 13, 3676. [Google Scholar] [CrossRef]
  157. Cherrad, I.; Dokkar, B.; Khenfer, N.; Benoumhani, S.; Benzid, M.C. Cooling improvement of an agricultural greenhouse using geothermal energy in a desert climate. Int. J. Energy Environ. Eng. 2023, 14, 211–228. [Google Scholar] [CrossRef]
  158. Agris, A.; Arnis, H.I.; Semjons, I.; Aivars, J.; Ugis, G.; Adolfs, R. Development of technological process solutions in modular system of solar electricity and heat supply for greenhouses. In Proceedings of the 22nd International Scientific Conference “Engineering for Rural Development”, Jelgava, Latvia, 24–26 May 2023; pp. 636–643. [Google Scholar]
  159. Kant, K.; Biwole, P.; Shamseddine, I.; Tlaiji, G.; Pennec, F. Advances in solar greenhouse systems for cultivation of agricultural products. In Solar Energy Advancements in Agriculture and Food Production Systems; Elsevier: Amsterdam, The Netherlands, 2022; pp. 77–111. [Google Scholar]
  160. Misra, D.; Ghosh, S. Evaporative cooling technologies for greenhouses: A comprehensive review. Agric. Eng. Int. CIGR J. 2018, 20, 1–15. [Google Scholar]
  161. Sethi, V.P.; Sharma, S.K. Survey of cooling technologies for worldwide agricultural greenhouse applications. Sol. Energy 2007, 81, 1447–1459. [Google Scholar] [CrossRef]
  162. Aljubury, I.M.A.; Ridha, H.D. Enhancement of evaporative cooling system in a greenhouse using geothermal energy. Renew. Energy 2017, 111, 321–331. [Google Scholar] [CrossRef]
  163. Liu, C.-H.; Ay, C.; Tsai, C.-Y.; Lee, M.-T. The application of passive radiative cooling in greenhouses. Sustainability 2019, 11, 6703. [Google Scholar] [CrossRef]
  164. Campiotti, C.A.; Morosinotto, G.; Puglisi, G.; Schettini, E.; Vox, G. Performance evaluation of a solar cooling plant applied for greenhouse thermal control. Agric. Agric. Sci. Procedia 2016, 8, 664–669. [Google Scholar] [CrossRef]
  165. Hughes, B.R.; Chaudhry, H.N.; Ghani, S.A. A review of sustainable cooling technologies in buildings. Renew. Sustain. Energy Rev. 2011, 15, 3112–3120. [Google Scholar] [CrossRef]
  166. Rhee, K.N.; Olesen, B.W.; Kim, K.W. Ten questions about radiant heating and cooling systems. Build. Environ. 2017, 112, 367–381. [Google Scholar] [CrossRef]
  167. Andresen, M.; Liserre, M. Impact of active thermal management on power electronics design. Microelectron. Reliab. 2014, 54, 1935–1939. [Google Scholar] [CrossRef]
  168. Miner, A.; Ghoshal, U. Limits of heat removal in microelectronic systems. IEEE Trans. Components Packag. Technol. 2006, 29, 743–749. [Google Scholar] [CrossRef]
  169. Paris, B.; Vandorou, F.; Balafoutis, A.T.; Vaiopoulos, K.; Kyriakarakos, G.; Manolakos, D.; Papadakis, G. Energy use in greenhouses in the EU: A review recommending energy efficiency measures and renewable energy sources adoption. Appl. Sci. 2022, 12, 5150. [Google Scholar] [CrossRef]
  170. Ferraro, V.; Bevilacqua, P.; Bruno, R.; Arcuri, N. Energy savings in greenhouses through the use of heat recovery systems. Tec. Ital.-Ital. J. Eng. Sci. 2019, 63, 467–473. [Google Scholar] [CrossRef]
  171. Tawalbeh, M.; Aljaghoub, H.; Alami, A.H.; Olabi, A.G. Selection criteria of cooling technologies for sustainable greenhouses: A comprehensive review. Therm. Sci. Eng. Prog. 2023, 38, 101666. [Google Scholar] [CrossRef]
  172. Guan, Y.; Meng, Q.; Ji, T.; Hu, W.; Li, W.; Liu, T. Experimental study of the thermal characteristics of a heat storage wall with micro-heat pipe array (MHPA) and PCM in solar greenhouse. Energy 2023, 264, 126183. [Google Scholar] [CrossRef]
  173. Sokolov, S.V. Optimization of greenhouse microclimate parameters considering the impact of CO2 and light. Eng. Sci. 2023, 10, G14–G21. [Google Scholar] [CrossRef]
  174. Katzin, D.; Marcelis, L.F.M.; van Mourik, S. Energy savings in greenhouses by transition from high-pressure sodium to LED lighting. Appl. Energy 2021, 281, 116019. [Google Scholar] [CrossRef]
  175. Paradiso, R.; Proietti, S. Light-quality manipulation to control plant growth and photomorphogenesis in greenhouse horticulture: The state of the art and the opportunities of modern LED systems. J. Plant Growth Regul. 2022, 41, 742–780. [Google Scholar] [CrossRef]
  176. Shen, L.; Lou, R.; Park, Y.; Guo, Y.; Stallknecht, E.J.; Xiao, Y.; Rieder, D.; Yang, R.; Runkle, E.S.; Yin, X. Increasing greenhouse production by spectral-shifting and unidirectional light-extracting photonics. Nat. Food 2021, 2, 434–441. [Google Scholar] [CrossRef]
  177. Bersani, C.; Ruggiero, C.; Sacile, R.; Soussi, A.; Zero, E. Internet of Things Approaches for Monitoring and Control of Smart Greenhouses in Industry 4.0. Energies 2022, 15, 3834. [Google Scholar] [CrossRef]
  178. Li, Y.; Ding, Y.; Li, D.; Miao, Z. Automatic carbon dioxide enrichment strategies in the greenhouse: A review. Biosyst. Eng. 2018, 171, 101–119. [Google Scholar] [CrossRef]
  179. Han, X.; Sun, Y.; Chen, J.; Wang, Z.; Qi, H.; Liu, Y.; Liu, Y. Effects of CO2 Enrichment on Carbon Assimilation, Yield and Quality of Oriental Melon Cultivated in a Solar Greenhouse. Horticulturae 2023, 9, 561. [Google Scholar] [CrossRef]
  180. Van Tuyll, A.; Graamans, L.; Boedijn, A. Carbon Dioxide Enrichment in a Decarbonised Future; Stichting Wageningen Research, Wageningen Plant Research, Business Unit: Wageningen, The Netherlands, 2022. [Google Scholar]
  181. Dion, L.M.; Lefsrud, M.; Orsat, V.; Cimon, C. Biomass gasification and syngas combustion for greenhouse CO2 enrichment. Bioresources 2013, 8, 1520–1538. [Google Scholar] [CrossRef]
  182. Kuroyanagi, T.; Yasuba, K.; Higashide, T.; Iwasaki, Y.; Takaichi, M. Efficiency of carbon dioxide enrichment in an unventilated greenhouse. Biosyst. Eng. 2014, 119, 58–68. [Google Scholar] [CrossRef]
  183. Wang, A.; Lv, J.; Wang, J.; Shi, K. CO2 enrichment in greenhouse production: Towards a sustainable approach. Front. Plant Sci. 2022, 13, 1029901. [Google Scholar] [CrossRef] [PubMed]
  184. Yang, S.-H.; Lee, C.G.; Ashtiani-Araghi, A.; Kim, J.Y.; Rhee, J.Y. Development and evaluation of combustion-type CO2 enrichment system connected to heat pump for greenhouses. Eng. Agric. Environ. Food 2014, 7, 28–33. [Google Scholar] [CrossRef]
  185. Kochhar, A.; Kumar, N. Wireless sensor networks for greenhouses: An end-to-end review. Comput. Electron. Agric. 2019, 163, 104877. [Google Scholar] [CrossRef]
  186. Bai, X.; Wang, Z.; Zou, L.; Alsaadi, F.E. Collaborative fusion estimation over wireless sensor networks for monitoring CO2 concentration in a greenhouse. Inf. Fusion 2018, 42, 119–126. [Google Scholar] [CrossRef]
  187. Ting, L.; Man, Z.; Yuhan, J.; Sha, S.; Yiqiong, J.; Minzan, L. Management of CO2 in a tomato greenhouse using WSN and BPNN techniques. Int. J. Agric. Biol. Eng. 2015, 8, 43–51. [Google Scholar]
  188. Mekki, M.; Abdallah, O. Development of a Wireless Sensors Network for Greenhouse Monitoring and Control. Indones. J. Electr. Eng. Inform. (IJEEI) 2017, 5, 270–274. [Google Scholar]
  189. Saínchez-Molina, J.A.; Reinoso, J.V.; Acieín, F.G.; Rodríquez, F.; Loípez, J.C. Development of a biomass-based system for nocturnal temperature and diurnal CO2 concentration control in greenhouses. Biomass Bioenergy 2014, 67, 60–71. [Google Scholar] [CrossRef]
  190. Ohyama, K.; Kozai, T.; Ishigami, Y.; Ohno, Y.; Toida, H.; Ochi, Y. A CO2 control system for a greenhouse with a high ventilation rate. In Proceedings of the International Conference on Sustainable Greenhouse Systems-Greensys2004, Leuven, Belgium, 26–29 September 2004; Volume 691, pp. 649–654. [Google Scholar]
  191. Berkovich, Y.A.; Konovalova, I.O.; Smolyanina, S.O.; Erokhin, A.N.; Avercheva, O.V.; Bassarskaya, E.M.; Kochetova, G.V.; Zhigalova, T.V.; Yakovleva, O.S.; Tarakanov, I.G. LED crop illumination inside space greenhouses. Reach 2017, 6, 11–24. [Google Scholar] [CrossRef]
  192. Cossu, M.; Yano, A.; Solinas, S.; Deligios, P.A.; Tiloca, M.T.; Cossu, A.; Ledda, L. Agricultural sustainability estimation of the European photovoltaic greenhouses. Eur. J. Agron. 2020, 118, 126074. [Google Scholar] [CrossRef]
  193. Afzali, S.; Mosharafian, S.; van Iersel, M.W.; Mohammadpour Velni, J. Development and implementation of an IoT-enabled optimal and predictive lighting control strategy in greenhouses. Plants 2021, 10, 2652. [Google Scholar] [CrossRef]
  194. Baglivo, C.; Mazzeo, D.; Panico, S.; Bonuso, S.; Matera, N.; Congedo, P.M.; Oliveti, G. Complete greenhouse dynamic simulation tool to assess the crop thermal well-being and energy needs. Appl. Therm. Eng. 2020, 179, 115698. [Google Scholar] [CrossRef]
  195. Gorjian, S.; Ebadi, H.; Najafi, G.; Chandel, S.S.; Yildizhan, H. Recent advances in net-zero energy greenhouses and adapted thermal energy storage systems. Sustain. Energy Technol. Assess. 2021, 43, 100940. [Google Scholar] [CrossRef]
  196. Amara, H.B.; Bouadila, S.; Guizani, A.; Fatnassi, H. Study of structural characteristics of wind-speed natural ventilation on single span greenhouse. In Proceedings of the 2020 11th International Renewable Energy Congress (IREC), Nabeul, Tunisia, 24–26 March 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1–6. [Google Scholar]
  197. Peretz, M.F.; Geoola, F.; Yehia, I.; Ozer, S.; Levi, A.; Magadley, E.; Brikman, R.; Rosenfeld, L.; Levy, A.; Kacira, M.; et al. Testing organic photovoltaic modules for application as greenhouse cover or shading element. Biosyst. Eng. 2019, 184, 24–36. [Google Scholar] [CrossRef]
  198. Amara, H.B.; Bouadila, S.; Fatnassi, H.; Arici, M.; Guizani, A.A. Climate assessment of greenhouse equipped with south-oriented PV roofs: An experimental and computational fluid dynamics study. Sustain. Energy Technol. Assess. 2021, 45, 101100. [Google Scholar]
  199. Nikolaou, G.; Neocleous, D.; Katsoulas, N.; Kittas, C. Irrigation of greenhouse crops. Horticulturae 2019, 5, 7. [Google Scholar] [CrossRef]
  200. Marcelis, L.F.M.; Costa, J.M.; Heuvelink, E. Achieving sustainable greenhouse production: Present status, recent advances and future developments. In Achieving Sustainable Greenhouse Cultivation; Burleigh Dodds Science Publishing: Cambridge, UK, 2019; pp. 1–14. [Google Scholar]
  201. Lambers, H.; Oliveira, R.S. Plant water relations. In Plant Physiological Ecology; Springer: Cham, Switzerland, 2019; pp. 187–263. [Google Scholar]
  202. Landis, T.D. Water Quality and Irrigation. In Nursery Manual for Native Plants: A Guide for Tribal Nurseries; US Department of Agriculture, Forest Service: Washington, DC, USA, 2009; No. 730; p. 177. [Google Scholar]
  203. Rodríguez, F.; Berenguel, M.; Guzmán, J.L.; Ramírez-Arias, A. Modeling and Control of Greenhouse Crop Growth; Springer: Cham, Switzerland, 2015. [Google Scholar]
  204. Incrocci, L.; Thompson, R.B.; Fernandez-Fernandez, M.D.; De Pascale, S.; Pardossi, A.; Stanghellini, C.; Rouphael, Y.; Gallardo, M. Irrigation management of European greenhouse vegetable crops. Agric. Water Manag. 2020, 242, 106393. [Google Scholar] [CrossRef]
  205. Murthy, B.Y.S.S.; Reddy, C.B.K.; Jilani, S.; Sindhwani, M. Smart Irrigation System. In Proceedings of the 2022 1st International Conference on Sustainable Technology for Power and Energy Systems (STPES), Coimbatore, India, 24–25 March 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 1–4. [Google Scholar]
  206. Setiowati, S.; Wardhani, R.N.; Azzam, A.; Rahmadhitya, A.A.; Adillah, R.F. Sprinkler Irrigation System for Pakcoy Cultivation Based on Mamdani Fuzzy Logic and LoRa Communication. In Proceedings of the 2023 6th International Conference of Computer and Informatics Engineering (IC2IE), Yogyakarta, Indonesia, 10–11 October 2023; IEEE: Piscataway, NJ, USA, 2023; pp. 81–86. [Google Scholar]
  207. Kumar, A.; Burdak, B.; Thakur, H.; Harshavardhan, S.; Rao, S.N.; Mrudula, P.; Aibel, H.P. A review on role of micro irrigation for modern agriculture. Pharma J. 2023, 12, 2585–2589. [Google Scholar]
  208. Martin-Gorriz, B.; Maestre-Valero, J.F.; Gallego-Elvira, B.; Marín-Membrive, P.; Terrero, P.; Martínez-Alvarez, V. Recycling drainage effluents using reverse osmosis powered by photovoltaic solar energy in hydroponic tomato production: Environmental footprint analysis. J. Environ. Manag. 2021, 297, 113326. [Google Scholar] [CrossRef]
  209. Jensen, M.H. Hydroponics worldwide. In Proceedings of the International Symposium on Growing Media and Hydroponics, Windsor, ON, Canada, 19–26 May 1997; Volume 481, pp. 719–730. [Google Scholar]
  210. Velazquez-Gonzalez, R.S.; Garcia-Garcia, A.L.; Ventura-Zapata, E.; Barceinas-Sanchez, J.D.O.; Sosa-Savedra, J.C. A review on hydroponics and the technologies associated for medium-and small-scale operations. Agriculture 2022, 12, 646. [Google Scholar] [CrossRef]
  211. Katsoulas, N.; Baille, A.; Kittas, C. Effect of misting on transpiration and conductances of a greenhouse rose canopy. Agric. For. Meteorol. 2001, 106, 233–247. [Google Scholar] [CrossRef]
  212. Grange, R.I.; Hand, D.W. A review of the effects of atmospheric humidity on the growth of horticultural crops. J. Hortic. Sci. 1987, 62, 125–134. [Google Scholar] [CrossRef]
  213. Raudales, R.E.; Fisher, P.R.; Hall, C.R. The cost of irrigation sources and water treatment in greenhouse production. Irrig. Sci. 2017, 35, 43–54. [Google Scholar] [CrossRef]
  214. Li, B.; Shi, B.; Yao, Z.; Shukla, M.K.; Du, T. Energy partitioning and microclimate of solar greenhouse under drip and furrow irrigation systems. Agric. Water Manag. 2020, 234, 106096. [Google Scholar] [CrossRef]
  215. Singh, A. Soil salinization management for sustainable development: A review. J. Environ. Manag. 2021, 277, 111383. [Google Scholar] [CrossRef]
  216. García-Sánchez, F.; Sánchez-Martínez, M.; Marín-Sánchez, A. Performance of a subsurface drip irrigation system in a Mediterranean horticultural crop. Agric. Water Manag. 2018, 204, 200–211. [Google Scholar]
  217. Durai, C.R.B.; Vipulan, B.; Khan, T.A.; Prakash, T.S.R. Solar powered automatic irrigation system. In Proceedings of the 2018 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India, 22–23 February 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 139–142. [Google Scholar]
  218. Zhang, M.Z.; Niu, W.Q.; Bai, Q.J.; Li, Y.; Wang, J.W.; Wang, Z.Q.; Zhang, Z.X. Improvement of quality and yield of greenhouse tomato (Solanum lycopersicum L.) plants by micro-sprinkler irrigation under plastic film. Appl. Ecol. Environ. Res. 2020, 18, 5. [Google Scholar] [CrossRef]
  219. Bronson, K.F.; Hunsaker, D.J.; Williams, C.F.; Thorp, K.R.; Rockholt, S.M.; Del Grosso, S.J.; Venterea, R.T.; Barnes, E.M. Nitrogen management affects nitrous oxide emissions under varying cotton irrigation systems in the Desert Southwest, USA. J. Environ. Qual. 2018, 47, 70–78. [Google Scholar] [CrossRef] [PubMed]
  220. Gultekin, R.; Avag, K.; Görgişen, C.; Öztürk, Ö.; Yeter, T.; Alsan, P.B. Effect of deficit irrigation practices on greenhouse gas emissions in drip irrigation. Sci. Hortic. 2023, 310, 111757. [Google Scholar] [CrossRef]
  221. Lucero-Vega, G.; Troyo-Dieguez, E.; Murillo-Amador, B.; Nieto-Garibay, A.; Ruíz-Espinoza, F.H.; Beltrán-Morañes, F.A.; Zamora-Salgado, S. Design of an underground irrigation system to decrease soil evaporation, as compared with two conventional methods. Agrociencia 2017, 51, 487–505. [Google Scholar]
  222. Dik, D.; Polyakova, E.; Chelovechkova, A.; Moskvin, V.; Nikiforova, T. The System of Environment Control of Botanic Garden Greenhouses. In Proceedings of the 2018 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), Vladivostok, Russia, 2–4 October 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–7. [Google Scholar]
  223. Yang, L.; Wu, Y.; Lu, C.; Yan, S.; Liu, H.; Luo, Y. Design and Optimization of Intelligent Greenhouse Automatic Control System. In Proceedings of the 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP), Hangzhou, China, 26–28 April 2023; IEEE: Piscataway, NJ, USA, 2023; pp. 316–319. [Google Scholar]
  224. Zhang, S.; Guo, Y.; Zhao, H.; Wang, Y.; Chow, D.; Fang, Y. Methodologies of control strategies for improving energy efficiency in agricultural greenhouses. J. Clean. Prod. 2020, 274, 122695. [Google Scholar] [CrossRef]
  225. Bagagiolo, G.; Matranga, G.; Cavallo, E.; Pampuro, N. Greenhouse Robots: Ultimate Solutions to Improve Automation in Protected Cropping Systems—A Review. Sustainability 2022, 14, 6436. [Google Scholar] [CrossRef]
  226. Van Mourik, S.; van der Tol, R.; Linker, R.; Reyes-Lastiri, D.; Kootstra, G.; Koerkamp, P.G.; van Henten, E.J. Introductory overview: Systems and control methods for operational management support in agricultural production systems. Environ. Model. Softw. 2021, 139, 105031. [Google Scholar] [CrossRef]
  227. Rayhana, R.; Xiao, G.; Liu, Z. Internet of things empowered smart greenhouse farming. IEEE J. Radio Freq. Identif. 2020, 4, 195–211. [Google Scholar] [CrossRef]
  228. Abbassy, M.M.; Ead, W.M. Intelligent greenhouse management system. In Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 6–7 March 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1317–1321. [Google Scholar]
  229. Iddio, E.; Wang, L.; Thomas, Y.; McMorrow, G.; Denzer, A. Energy efficient operation and modeling for greenhouses: A literature review. Renew. Sustain. Energy Rev. 2020, 117, 109480. [Google Scholar] [CrossRef]
  230. Sumalan, R.L.; Stroia, N.; Moga, D.; Muresan, V.; Lodin, A.; Vintila, T.; Popescu, C.A. A Cost-effective embedded platform for greenhouse environment control and remote monitoring. Agronomy 2020, 10, 936. [Google Scholar] [CrossRef]
  231. Li, H.; Guo, Y.; Zhao, H.; Wang, Y.; Chow, D. Towards automated greenhouse: A state of the art review on greenhouse monitoring methods and technologies based on internet of things. Comput. Electron. Agric. 2021, 191, 106558. [Google Scholar] [CrossRef]
  232. Lee, S.; Lee, I.; Yeo, U.; Kim, R.; Kim, J. Optimal sensor placement for monitoring and controlling greenhouse internal environments. Biosyst. Eng. 2019, 188, 190–206. [Google Scholar] [CrossRef]
  233. Tekcin, M.; Tuzer Hamzaoglu, D.R.; Kursun, S. Flexible humidity sensor for smart agricultural applications. Flex. Print. Electron. 2023, 8, 035003. [Google Scholar] [CrossRef]
  234. Gupta, G.S.; Quan, V.M. Multi-sensor integrated system for wireless monitoring of greenhouse environment. In Proceedings of the 2018 IEEE Sensors Applications Symposium (SAS), Seoul, Republic of Korea, 12–14 March 2018; pp. 1–6. [Google Scholar] [CrossRef]
  235. Ajani, O.S.; Usigbe, J.; Aboyeji, E.; Uyeh, D.D.; Ha, Y.; Park, T.; Mallipeddi, R. Greenhouse Micro-Climate Prediction Based on Fixed Sensor Placements: A Machine Learning Approach. Mathematics 2023, 11, 3052. [Google Scholar] [CrossRef]
  236. Fletcher, R.; Fisher, D. A Miniature Sensor for Measuring Reflectance, Relative Humidity, and Temperature: A Greenhouse Example. Agric. Sci. 2018, 9, 1516–1527. [Google Scholar] [CrossRef]
  237. Shen, D.; Xiao, M.; Xiao, Y.; Zou, G.; Hu, L.; Zhao, B.; Liu, L.; Duley, W.W.; Zhou, Y. Self-Powered, Rapid-Response, and Highly Flexible Humidity Sensors Based on Moisture-Dependent Voltage Generation. ACS Appl. Mater. Interfaces 2019, 11, 14249–14255. [Google Scholar] [CrossRef] [PubMed]
  238. Lan, L.; Le, X.; Dong, H.; Xie, J.; Ying, Y.; Ping, J. One-step and large-scale fabrication of flexible and wearable humidity sensor based on laser-induced graphene for real-time tracking of plant transpiration at bio-interface. Biosens. Bioelectron. 2020, 165, 112360. [Google Scholar] [CrossRef] [PubMed]
  239. Danita, M.; Mathew, B.; Shereen, N.; Sharon, N.; Paul, J. IoT Based Automated Greenhouse Monitoring System. In Proceedings of the 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 14–15 June 2018; pp. 1933–1937. [Google Scholar] [CrossRef]
  240. Zarnescu, A.; Ungurelu, R.; Vărzaru, G. Controlling the Temperature and Humidity in a Greenhouse. In Proceedings of the 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME), Cluj-Napoca, Romania, 23–26 October 2019; pp. 168–171. [Google Scholar] [CrossRef]
  241. Meili, L.; Yankang, B. Embedded Automatic Control System for Temperature, Humidity and Light Intensity in Agricultural Greenhouses. In Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control, Stockholm, Sweden, 21–23 September 2018. [Google Scholar] [CrossRef]
  242. Valdivia, C.H.G.; Escobedo, J.L.C.; Durán-Muñoz, H.; Berumen, J.; Ortiz, A.A.; Guirette, O.A.; Arroyo, A.; Brizuela, J.; Gómez, F.A.; Blanco-Ortega, A.; et al. Implementation of Virtual Sensors for Monitoring Temperature in Greenhouses Using CFD and Control. Sensors 2018, 19, 60. [Google Scholar] [CrossRef] [PubMed]
  243. Wardani, I.K.; Ichniarsyah, A.; Telaumbanua, M.; Priyonggo, B.; Fil’aini, R.; Mufidah, Z.; Dewangga, D.A. The feasibility study: Accuracy and precision of DHT 22 in measuring the temperature and humidity in the greenhouse. IOP Conf. Ser. Earth Environ. Sci. 2023, 1230, 012146. [Google Scholar] [CrossRef]
  244. Bhujel, A.; Basak, J.; Khan, F.; Arulmozhi, E.; Jaihuni, M.; Sihalath, T.; Lee, D.; Park, J.; Kim, H. Sensor Systems for Greenhouse Microclimate Monitoring and Control: A Review. J. Biosyst. Eng. 2020, 45, 341–361. [Google Scholar] [CrossRef]
  245. Guzman, B.G.; Talavante, J.; Frometa, D.F.; Mir, M.S.; Giustiniano, D.; Obraczka, K.; Loik, M.E.; Childress, S.; Wong, D.G. Toward Sustainable Greenhouses Using Battery-Free LiFi-Enabled Internet of Things. IEEE Commun. Mag. 2023, 61, 129–135. [Google Scholar] [CrossRef]
  246. Hamad, I.H.; Chouchaine, A.; Bouzaouache, H. On modeling greenhouse air-temperature: An experimental validation. In Proceedings of the 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD), Monastir, Tunisia, 23–26 March 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 353–358. [Google Scholar]
  247. Pierart, F.G.; Sanhueza, D.A.V.; Riquelme, S. Greenhouse Parametric Computational Fluid Dynamic model. In Proceedings of the 2022 IEEE International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA), Valparaíso, Chile, 7–9 December 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 1–6. [Google Scholar]
  248. Ghani, S.; El-Bialy, E.M.A.A.; Bakochristou, F.; Rashwan, M.M.; Abdelhalim, A.M.; Ismail, S.M.; Ben, P. Experimental and numerical investigation of the thermal performance of evaporative cooled greenhouses in hot and arid climates. Sci. Technol. Built Environ. 2020, 26, 141–160. [Google Scholar] [CrossRef]
  249. Choab, N.; Allouhi, A.; El Maakoul, A.; Kousksou, T.; Saadeddine, S.; Jamil, A. Review on greenhouse microclimate and application: Design parameters, thermal modeling and simulation, climate controlling technologies. Sol. Energy 2019, 191, 109–137. [Google Scholar] [CrossRef]
  250. Miyoshi, T.; Kondo, K.; Terasaki, K. Big ensemble data assimilation in numerical weather prediction. Computer 2015, 48, 15–21. [Google Scholar] [CrossRef]
  251. Aaslyng, J.M.; Ehler, N.; Jakobsen, L. Climate control software integration with a greenhouse environmental control computer. Environ. Model. Softw. 2005, 20, 521–527. [Google Scholar] [CrossRef]
  252. Du, J.; Bansal, P.; Huang, B. Simulation model of a greenhouse with a heat-pipe heating system. Appl. Energy 2012, 93, 268–276. [Google Scholar] [CrossRef]
  253. Fitz-Rodríguez, E.; Kubota, C.; Giacomelli, G.A.; Tignor, M.E.; Wilson, S.B.; McMahon, M. Dynamic modeling and simulation of greenhouse environments under several scenarios: A web-based application. Comput. Electron. Agric. 2010, 70, 105–116. [Google Scholar] [CrossRef]
  254. Ma, D.; Carpenter, N.; Maki, H.; Rehman, T.U.; Tuinstra, M.R.; Jin, J. Greenhouse environment modeling and simulation for microclimate control. Comput. Electron. Agric. 2019, 162, 134–142. [Google Scholar] [CrossRef]
  255. Nguyen, N.M.; Tran, H.T.; Duong, M.V.; Bui, H.; Tran, K. Differentiable Physics-based Greenhouse Simulation. arXiv 2022, arXiv:2211.11502. [Google Scholar]
  256. Molina-Aiz, F.D.; Fatnassi, H.; Boulard, T.; Roy, J.C.; Valera, D.L. Comparison of finite element and finite volume methods for simulation of natural ventilation in greenhouses. Comput. Electron. Agric. 2010, 72, 69–86. [Google Scholar] [CrossRef]
  257. Kim, R.; Kim, J.; Lee, I.; Yeo, U.; Lee, S.; Decano-Valentin, C. Development of three-dimensional visualisation technology of the aerodynamic environment in a greenhouse using CFD and VR technology, part 1: Development of VR a database using CFD. Biosyst. Eng. 2021, 207, 12–32. [Google Scholar] [CrossRef]
  258. Boulard, T.; Roy, J.; Pouillard, J.B.; Fatnassi, H.; Grisey, A. Modelling of micrometeorology, canopy transpiration and photosynthesis in a closed greenhouse using computational fluid dynamics. Biosyst. Eng. 2017, 158, 110–133. [Google Scholar] [CrossRef]
  259. Taki, M.; Ajabshirchi, Y.; Ranjbar, S.; Rohani, A.; Matloobi, M. Modeling and experimental validation of heat transfer and energy consumption in an innovative greenhouse structure. Inf. Process. Agric. 2016, 3, 157–174. [Google Scholar] [CrossRef]
  260. Gao, Y.; Dong, J.; Isabella, O.; Santbergen, R.; Tan, H.; Zeman, M.; Zhang, G. Modeling and analyses of energy performances of photovoltaic greenhouses with sun-tracking functionality. Appl. Energy 2019, 233, 424–442. [Google Scholar] [CrossRef]
  261. Lachouri, C.E.; Mansouri, K.; Lafifi, M.M. Greenhouse Climate Modeling Using Fuzzy Neural Network Machine Learning Technique. Rev. Intell. Artif. 2022, 36, 925. [Google Scholar] [CrossRef]
  262. Li, K.; Xue, W.; Mao, H.; Chen, X.; Jiang, H.; Tan, G. Optimizing the 3D Distributed Climate inside Greenhouses Using Multi-Objective Optimization Algorithms and Computer Fluid Dynamics. Energies 2019, 12, 2873. [Google Scholar] [CrossRef]
  263. Chen, J.; Xu, F.; Tan, D.; Shen, Z.; Zhang, L.; Ai, Q. A control method for agricultural greenhouses heating based on computational fluid dynamics and energy prediction model. Appl. Energy 2015, 141, 106–118. [Google Scholar] [CrossRef]
  264. Rezvani, S.M.-E.; Shamshiri, R.; Hameed, I.; Abyane, H.Z.; Godarzi, M.; Momeni, D.; Balasundram, S.K. Greenhouse Crop Simulation Models and Microclimate Control Systems, A Review; IntechOpen: Rijeka, Croatia, 2021. [Google Scholar] [CrossRef]
  265. Weng, Y.; Wang, X.; Hua, J.; Wang, H.; Kang, M. Greenhouse Environment Control based on Computational Experiments. In Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence, Qingdao, China, 3–6 July 2020. [Google Scholar] [CrossRef]
  266. Maraveas, C. Incorporating artificial intelligence technology in smart greenhouses: Current State of the Art. Appl. Sci. 2022, 13, 14. [Google Scholar] [CrossRef]
  267. Kim, W.S.; Lee, W.S.; Kim, Y.J. A review of the applications of the internet of things (IoT) for agricultural automation. J. Biosyst. Eng. 2020, 45, 385–400. [Google Scholar] [CrossRef]
  268. Luna, D.F.O.; Ruiz, P.A.M. Automation and control of greenhouse implemented technologies: A review. Visión Electrónica 2019, 2, 381–394. [Google Scholar] [CrossRef]
  269. Gullino, M.L.; Albajes, R.; Nicot, P.C. Integrated Pest and Disease Management in Greenhouse Crops; Springer: Cham, Switzerland, 2020; Volume 9. [Google Scholar]
  270. Ullah, I.; Fayaz, M.; Aman, M.; Kim, D. Toward Autonomous Farming—A Novel Scheme Based on Learning to Prediction and Optimization for Smart Greenhouse Environment Control. IEEE Internet Things J. 2022, 9, 25300–25323. [Google Scholar] [CrossRef]
  271. Moradi, P.; Sadighi, H.; Chizari, M.; Sharifikia, M. Identification of Strategies for Application of Pro-Environmental Technologies to Produce Greenhouse Vegetables. J. Agric. Sci. Technol. 2020, 22, 653–666. [Google Scholar]
  272. Salimi, M.; Pourdarbani, R.; Nouri, B.A. Factors affecting the adoption of agricultural automation using Davis’s acceptance model (case study: Ardabil). Acta Technol. Agric. 2020, 23, 30–39. [Google Scholar] [CrossRef]
  273. Van Henten, E.J.; Hemming, J.; Van Tuijl, B.A.J.; Kornet, J.G.; Meuleman, J.; Bontsema, J.; Van Os, E.A. An autonomous robot for harvesting cucumbers in greenhouses. Auton. Robots 2002, 13, 241–258. [Google Scholar] [CrossRef]
  274. Komarchuk, D.S.; Gunchenko, Y.A.; Pasichnyk, N.A.; Opryshko, O.A.; Shvorov, S.A.; Reshetiuk, V. Use of Drones in Industrial Greenhouses. In Proceedings of the 2021 IEEE 6th International Conference on Actual Problems of Unmanned Aerial Vehicles Development (APUAVD), Kyiv, Ukraine, 21–23 September 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 184–187. [Google Scholar]
  275. Aslan, M.F.; Durdu, A.; Sabanci, K.; Ropelewska, E.; Gültekin, S.S. A comprehensive survey of the recent studies with UAV for precision agriculture in open fields and greenhouses. Appl. Sci. 2022, 12, 1047. [Google Scholar] [CrossRef]
  276. Sivagami, A.; Kandavalli, M.A.; Yakkala, B. Design and Evaluation of an Automated Monitoring and Control System for Greenhouse Crop Production. Next-Gener. Greenh. Food Secur. 2021, 1, 149–167. [Google Scholar] [CrossRef]
  277. Ramasamy, P.; Pandian, N.; Mayathevar, K.; Ravindran, R.; Kandula, S.R.; Devadoss, S.; Kuppusamy, S. Design of Arduino UNO based smart irrigation system for real time applications. Int. J. Reconfigurable Embed. Syst. (IJRES) 2024, 13, 105–110. [Google Scholar] [CrossRef]
  278. Salman, M.; Mahdi, M.M.; Ahmed, M.K. Optimization of solar powered air conditioning system using alternating Peltier power supply. Bull. Electr. Eng. Inform. 2024, 13, 20–30. [Google Scholar] [CrossRef]
  279. Prabha, C.; Pathak, A. Enabling Technologies in Smart Agriculture: A Way Forward Towards Future Fields. In Proceedings of the 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT), Gharuan, India, 5–6 May 2023; pp. 821–826. [Google Scholar] [CrossRef]
  280. Lin, T.; Wang, H.; Li, Z.; Zhu, J. A Novel Design of a Unilateral Nuclear Magnetic Resonance Sensor for Soil Moisture Detection Based on a Simplified Analytical Model. IEEE Trans. Geosci. Remote Sens. 2024, 62, 1–11. [Google Scholar] [CrossRef]
  281. Chakraborty, A.; Islam, M.; Dhar, A.; Hossain, M.S. IoT Based Greenhouse Environment Monitoring and Smart Irrigation System for Precision Farming Technology. In Proceedings of the 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET), Istanbul, Turkeym, 23–25 November 2022; pp. 123–128. [Google Scholar] [CrossRef]
  282. Biradar, K.; Meng, Q. Nutrient Solution Application of a Calcium-mobilizing Biostimulant Mitigates Tipburn without Decreasing Biomass of Greenhouse Hydroponic Lettuce. HortScience 2024, 59, 92–98. [Google Scholar] [CrossRef]
  283. Omara, A. Improving Water Use Efficiency of Maize Under A Laser Spray Irrigation System. Alex. J. Soil Water Sci. 2024, 8, 1–22. [Google Scholar] [CrossRef]
  284. Mohagheghi, A.; Moallem, M. Measuring Photosynthetic Photon Flux Density in the Blue and Red Spectrum for Horticultural Lighting Using Machine Learning Methods. IEEE Trans. Instrum. Meas. 2024, 73, 1–10. [Google Scholar] [CrossRef]
  285. Emmi, L.; Fernández, R.; Guerrero, J.M. Editorial: Robotics for smart farms. Front. Robot. AI 2023, 9, 123–134. [Google Scholar] [CrossRef]
  286. Mujtahidin, M.H.; Shah, A.F.M.; Jais, A.S.A.; Annuar, K.A.M.; Sapiee, M.R. Design and development of control and monitoring hydroponic system. Int. J. Reconfigurable Embed. Syst. (IJRES) 2024, 13, 41–51. [Google Scholar] [CrossRef]
  287. Shamshiri, R.; Hameed, I.; Thorp, K.; Balasundram, S.K.; Shafian, S.; Fatemieh, M.; Sultan, M.; Mahns, B.; Samiei, S. Greenhouse Automation Using Wireless Sensors and IoT Instruments Integrated with Artificial Intelligence; IntechOpen: Rijeka, Croatia, 2021. [Google Scholar] [CrossRef]
  288. Wang, X.; Zhao, M.; Liu, B.; Zou, C.; Sun, Y.; Wu, G.; Zhang, Q.; Jin, G.; Jin, Z.; Chadwick, D.; et al. Integrated systematic approach increase greenhouse tomato yield and reduce environmental losses. J. Environ. Manag. 2020, 266, 110569. [Google Scholar] [CrossRef]
  289. Lin, Y.-B.; Liu, C.-Y.; Chen, W.-L.; Chang, C.-H.; Ng, F.-L.; Yang, K.; Hsung, J. IoT-Based Strawberry Disease Detection With Wall-Mounted Monitoring Cameras. IEEE Internet Things J. 2024, 11, 1439–1451. [Google Scholar] [CrossRef]
  290. Castañeda-Miranda, A.; Castaño-Meneses, V.M. Smart frost measurement for anti-disaster intelligent control in greenhouses via embedding IoT and hybrid AI methods. Measurement 2020, 164, 108043. [Google Scholar] [CrossRef]
  291. Simon, J.; Petkovič, I.; Petkovic, D.; Petkovics, Á. Navigation and Applicability of Hexa Rotor Drones in Greenhouse Environment. Teh. Vjesn.-Tech. Gaz. 2018, 25, 249–255. [Google Scholar] [CrossRef]
  292. Cuce, E.; Harjunowibowo, D.; Cuce, P.M. Renewable and sustainable energy saving strategies for greenhouse systems: A comprehensive review. Renew. Sustain. Energy Rev. 2016, 64, 34–59. [Google Scholar] [CrossRef]
  293. Dalai, R.; Senapati, K.K. An Intelligent Vision based Pest Detection System Using RCNN based Deep Learning Mechanism. In Proceedings of the 2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC), Nagercoil, India, 7–8 March 2019; pp. 1–4. [Google Scholar] [CrossRef]
  294. Skripko, L.P.; Skripko, A. Automated Control Systems in Greenhouses. Herald Dagestan State Tech. Univ. Tech. Sci. 2023, 50, 150–155. [Google Scholar] [CrossRef]
  295. Yan, S.-R.; Fazilati, M.; Samani, N.; Ghasemi, H.; Toghraie, D.; Nguyen, Q.; Karimipour, A. Energy efficiency optimization of the waste heat recovery system with embedded phase change materials in greenhouses: A thermo-economic-environmental study. J. Energy Storage 2020, 30, 101445. [Google Scholar] [CrossRef]
Figure 1. Integrative model of a greenhouse: illustrates the interconnection between site selection, construction, water resource management, and climate management, highlighting the importance of a multidisciplinary approach to maximizing production efficiency and environmental sustainability in modern greenhouses.
Figure 1. Integrative model of a greenhouse: illustrates the interconnection between site selection, construction, water resource management, and climate management, highlighting the importance of a multidisciplinary approach to maximizing production efficiency and environmental sustainability in modern greenhouses.
Applsci 14 06158 g001
Figure 2. The sun’s daily rays fall on a greenhouse oriented east–west.
Figure 2. The sun’s daily rays fall on a greenhouse oriented east–west.
Applsci 14 06158 g002
Figure 3. The sun’s daily rays fall on a greenhouse with a north–south orientation.
Figure 3. The sun’s daily rays fall on a greenhouse with a north–south orientation.
Applsci 14 06158 g003
Figure 4. Main Greenhouse Shapes.
Figure 4. Main Greenhouse Shapes.
Applsci 14 06158 g004
Figure 5. Interactions of solar radiation with a material. The diagram illustrates the three fundamental processes: reflection, absorption, and transmission. Incident solar radiation is partially reflected off the surface, absorbed by the material and transmitted through it.
Figure 5. Interactions of solar radiation with a material. The diagram illustrates the three fundamental processes: reflection, absorption, and transmission. Incident solar radiation is partially reflected off the surface, absorbed by the material and transmitted through it.
Applsci 14 06158 g005
Figure 6. Transmittance of Greenhouse Covering Materials [7].
Figure 6. Transmittance of Greenhouse Covering Materials [7].
Applsci 14 06158 g006
Figure 7. Greenhouse with gable roof (Even-span). (a) Greenhouse with ventilation opening; (b) Greenhouse with north wall.
Figure 7. Greenhouse with gable roof (Even-span). (a) Greenhouse with ventilation opening; (b) Greenhouse with north wall.
Applsci 14 06158 g007
Figure 10. Greenhouses with different heating and cooling methods. (a) Scheme of the experimental arrangement in a test greenhouse with the solar groundwater heating system [155]; (b) Schematic of the experimental arrangement in a test greenhouse with the air-to-ground geothermal heating system [10].
Figure 10. Greenhouses with different heating and cooling methods. (a) Scheme of the experimental arrangement in a test greenhouse with the solar groundwater heating system [155]; (b) Schematic of the experimental arrangement in a test greenhouse with the air-to-ground geothermal heating system [10].
Applsci 14 06158 g010
Figure 11. Adapted from Van Mourik et al. [226], this figure presents flowcharts that illustrate four levels of automation in the operational management of agricultural greenhouses. 1. Traditional Agriculture: Manual operations performed by farmers, such as planting and irrigation, without technological assistance. 2. Manual Control: Farmers use sensing devices to guide the manual operation of actuators. 3. Supervised Control: Low-level automatic adjustments are made with human supervision, based on sensory data and external predictions. 4. Automatic Control: A fully automated closed control system integrates sensors and actuators, significantly reducing the farmer’s role in daily greenhouse operations.
Figure 11. Adapted from Van Mourik et al. [226], this figure presents flowcharts that illustrate four levels of automation in the operational management of agricultural greenhouses. 1. Traditional Agriculture: Manual operations performed by farmers, such as planting and irrigation, without technological assistance. 2. Manual Control: Farmers use sensing devices to guide the manual operation of actuators. 3. Supervised Control: Low-level automatic adjustments are made with human supervision, based on sensory data and external predictions. 4. Automatic Control: A fully automated closed control system integrates sensors and actuators, significantly reducing the farmer’s role in daily greenhouse operations.
Applsci 14 06158 g011
Figure 12. Temperature distribution and air flow in a greenhouse. The top image shows the temperature distribution with a color scale ranging from 298K to 310K, indicating zones of different temperatures in and around the greenhouse. The bottom image shows the airflow pattern around the greenhouse, with arrows indicating the direction and intensity of air movement [262].
Figure 12. Temperature distribution and air flow in a greenhouse. The top image shows the temperature distribution with a color scale ranging from 298K to 310K, indicating zones of different temperatures in and around the greenhouse. The bottom image shows the airflow pattern around the greenhouse, with arrows indicating the direction and intensity of air movement [262].
Applsci 14 06158 g012
Figure 13. Cucumber plant monitoring devices in a greenhouse environment. On the left, a sensor is attached to a cucumber stem to monitor the plant’s growth and health. On the right, a sensor attached to a cucumber leaf to measure photosynthesis and other vital functions [199].
Figure 13. Cucumber plant monitoring devices in a greenhouse environment. On the left, a sensor is attached to a cucumber stem to monitor the plant’s growth and health. On the right, a sensor attached to a cucumber leaf to measure photosynthesis and other vital functions [199].
Applsci 14 06158 g013
Table 1. Recent Investigations supporting the influence of greenhouse location and orientation on energy efficiency.
Table 1. Recent Investigations supporting the influence of greenhouse location and orientation on energy efficiency.
Study TitlePurpose of the StudyMethodology UsedMain Results
A computational model to determine the optimal orientation for solar greenhouses located at different latitudes in China [13]Determine the optimal orientation of solar greenhouses at different latitudes in China.Computational model based on the law of solar trajectory and thermal balance theory using the EnergyPlus software.This paper proposed a calculation model to determine the optimal orientation of greenhouses to maximize solar energy harvesting at different latitudes in China. Validation has shown that the method can predict optimal orientations that result in significant energy savings for heating greenhouses, especially in northern China.
Development and optimization of a building energy simulation model to study the effect of greenhouse design parameters [43]Evaluate the effect of greenhouse design parameters on energy conservation.Building Energy Simulation (BES) using TRNSYS.The east–west orientation, a Gothic-shaped roof, and the double-glazed PMMA (Polymethyl Methacrylate) roofing are the most effective design parameters for energy conservation in a greenhouse. In addition, natural ventilation has been shown to be effective in reducing the internal temperature of the greenhouse, which decreases the energy demand needed for cooling.
The orientation effect of the agricultural tunnel greenhouse on aerodynamic and energy properties [31]Analyze the effect of greenhouse orientation on aerodynamic and energetic properties.Use of Fluent software for aerodynamic and thermal analysis, adopting the finite volumes method to solve elliptic partial differential equations.Identification of the critical value of the Reynolds number, R e = 10 6 , which marks the transition point from the laminar to the turbulent regime. This value corresponds to the minimum drag coefficient and the minimum temperature gain, evidencing the significant impact of wind speed on the internal temperature of the greenhouse and on the aerodynamic drag coefficient, especially in the passage of air through the external walls of the greenhouse.
Achieving net zero energy greenhouses by integrating semitransparent organic solar cells (OSCs) [44]Integrate semi-transparent OSCs into greenhouses to achieve net-zero energy.Dynamic energy modeling conducted in MATLAB.Greenhouses equipped with semi-transparent OSCs can produce more energy than they consume in hot and moderate climates, offering a sustainable solution for agriculture by reducing energy consumption and minimizing the loss of light needed for plant growth.
Computational Modeling of the Thermal Behavior of a Greenhouse [45]Study different greenhouse structures to optimize thermal performance.Computational simulation using EnergyPlus and DesignBuilder.Adjustments in orientation and construction can significantly reduce energy consumption, with emphasis on east–west orientation and the use of double polyethylene roofing that can decrease energy consumption by up to 57% in Lisbon and 43% in Ostersund during the colder months.
Table 2. Recent Investigations on the Impact of Solar Radiation on the Energy Efficiency of Greenhouses.
Table 2. Recent Investigations on the Impact of Solar Radiation on the Energy Efficiency of Greenhouses.
Study TitlePurpose of the StudyMethodology UsedMain Results
Solar radiation allocation and spatial distribution in Chinese solar greenhouses: Model development and application [53]Quantitative assessment of the light environment in Chinese solar greenhouses, analyzing the shape of the greenhouse, materials and solar radiation.Evaluate the model under different climatic conditions, demonstrating its effectiveness.Develop a mathematical model that optimizes the distribution of light inside Chinese solar greenhouses, improving not only cultivation but also thermal efficiency near the north wall.
A review on opportunities for implementation of solar energy technologies in agricultural
greenhouses [64]
Review the progress of solar greenhouses by investigating integration with solar energy technologies, including photovoltaic, photovoltaic-thermal, and solar thermal collectors.Review of the literature on the integration of solar energy technologies in agricultural greenhouses, as well as the use of thermal energy storage units (TES).Identification of opportunities and challenges in the implementation of solar energy technologies in greenhouses Emphasis on the efficiency of PVT modules in the production of heat and electricity and the positive impact of TES systems on the thermal performance of solar greenhouses, increasing it by 29%.
Design challenges of agricultural greenhouses in hot and arid environments—A
review [65]
Review on greenhouse design features important for efficient operation in hot and arid environments, such as dimensions, orientation, shapes, roofing materials, and shading.Review of previous research on greenhouse design characteristics, including effective cooling methodologies and operating strategies for maintaining satisfactory climatic conditions.Review highlights sustainable design and operation strategies for greenhouses in arid environments, focusing on ventilation, cooling, and climate control for energy efficiency and water conservation.
Solar greenhouses: Climates, glass selection, and plant well-being [66]To analyze the performance of Venlo-type solar greenhouses in 48 locations around the world, identifying the best transparent envelope solution for different latitudes.Use of TRNsys software for simulations, considering different thermal phenomena, exact 3D position of lamps, evapotranspiration of soil and plants, and coefficients of convective heat transfer.Optimal glass solutions work better in winter than in summer. The ideal choice of glass should be combined with effective natural ventilation to prevent internal overheating.
Analytical model for solar radiation transmitting the curved transparent surface of solar greenhouse [50]Develop an improved mathematical model to calculate the total solar radiation captured by the curved surface of a solar greenhouse.Use of the MATLAB platform to calculate the total solar radiation, considering the radiation reflected from the external soil, with verification of the results.The radiation reflected from the ground outside is important by the same order of magnitude as scattered radiation, and reflected sunlight should not be overlooked, especially for cities at high altitudes.
Achieving Net Zero Energy Greenhouses by Integrating Semitransparent Organic Solar Cells (OSCs) [44]Evaluate the integration of OSCs in greenhouses to reduce energy consumption and achieve net-zero energy while minimizing the attenuation of sunlight useful to plants.Integration of OSCs in the greenhouse and the evaluation of the benefits of this integration in the net energy demand of greenhouses in the U.S., using the energy balance model.The use of OSC systems can have an annual energy surplus in warm and moderate climates. OSCs are excellent candidates for implementation in greenhouses and offer a significant opportunity to diversify sustainable energy generation technology.
Energy and optical analysis of photovoltaic thermal integrated with rotary linear curved Fresnel lens inside a Chinese solar greenhouse [59]Analyze the energetic and optical performance of a thermal photovoltaic system integrated with a rotating linear curved Fresnel lens inside a Chinese solar greenhouse.Implementation of an integrated CPV/T system with a cylindrical Fresnel lens in a Chinese greenhouse, performance evaluation through optical simulations, and experimental studies.It has achieved a non-plantable space utilization of 18.2%, maximum power generation efficiency of 18% at noon, maximum thermal efficiency of 45%, and total energy efficiency of 55%, offering an integrated solution to improve energy efficiency.
Table 3. Advantages, Disadvantages and Recommended Climate of Greenhouse Forms.
Table 3. Advantages, Disadvantages and Recommended Climate of Greenhouse Forms.
Shape of the GreenhouseAdvantagesDisadvantagesRecommended Climate
Flat Greenhouse [7,65]
-
Ease of construction
-
Reduced cost
-
Poor drainage
-
Snow accumulation
Seasoned
Arch Greenhouses (Quonset) [74,76,77]
-
Good wind resistance
-
Even distribution of light
-
Limited height in the center
-
Difficulty installing equipment
Several
Geodesic greenhouse [78]
-
Excellent weather resistance
-
Pleasant aesthetics
-
Higher cost
-
Construction complexity
Several
Chapel Greenhouse [7,71]
-
Additional vertical space
-
Good ventilation
-
Higher construction cost
-
Structural complexity
Seasoned
Tunnel Greenhouse [79,80,81]
-
Easy to build and expand
-
Cost-efficient
-
Limited internal height
-
Vulnerable to damage from strong wind
Temperate/Hot
Greenhouse in Saw or Chapel [28]
-
Optimized ventilation
-
Good access to natural light
-
More complex construction
-
Vulnerable to wind damage
Hot
Table 4. Summary of the different shapes of greenhouses.
Table 4. Summary of the different shapes of greenhouses.
Greenhouse ShapeDescriptionRecommendation
Flat roof [7,65]The greenhouse has a roof with parts sloping in opposite directions, but without an arched shape.Crops that need lots of natural light and fresh air, such as herbs or flowers.
Arch Greenhouses (Quonset) [74,76,77]The greenhouse has an arched roof, providing more vertical space for plants.Crops that require more vertical space.
Geodesic greenhouse [78]The greenhouse is supported by pillars offering more vertical space for plants.Crops that need more vertical space, such as climbing plants or fruit trees. Allows for a more even distribution of sunlight.
Chapel greenhouses [82,83,84]The greenhouse has parts that are tilted in opposite directions, like a traditional form of greenhouse.Crops that need plenty of natural light and fresh air, such as herbs or flowers.
Tunnel [12,81]Tunnel-shaped greenhouse covered by a plastic membrane or other similar material.Grow plants in cool weather or extend the growing season of warm-weather plants.
Table 5. Recent investigations on the influence of greenhouse shape on energy performance.
Table 5. Recent investigations on the influence of greenhouse shape on energy performance.
Study TitlePurpose of the StudyMethodology UsedMain Results
Solar energy conservation in greenhouse: Thermal analysis and experimental validation [87]Investigate six common forms of greenhouses from an energy consumption point of viewSteady-state analysis and experimental validation using AutoeCADand MATLABNorth wall insulation can reduce greenhouse heating demand by up to 31.7%. The correlation coefficient and the mean percentage error of the developed model were calculated at 0.79 and −2.34%, respectively.
Simulation of thermal performance of solar greenhouse in the north-west of Iran: An experimental validation [88]Compare six forms of greenhouses from the point of view of solar radiation availabilityDynamic model and experimental validation using MATLABThe east–west oriented regular span greenhouse captured about 8% more solar radiation throughout the year. The use of a brick wall in the north reduced the energy loss by radiation. The mean transmission of coverage was 0.76 over the course of the study, with a good correlation between measured and predicted data.
Effect of greenhouse design parameters on the heating and cooling requirement of greenhouses in Moroccan climatic conditions [85]Investigate design parameters that affect the thermal behavior and heating/cooling energy requirement of a greenhouse in Agadir, MoroccoThermal modeling using TRNSYS software, considering plant evapotranspirationThe east–west orientation is optimal, reducing the annual cost of air conditioning by 9.28% compared to the north–south orientation. The Quonset shape is the optimal one, saving 14.44% of the annual cost compared to the Even-span shape.
Optimal solar greenhouses design using multiobjective genetic algorithm [86]Improving the energy efficiency of solar greenhouses by optimizing their designApplication of a multi-objective genetic algorithm to optimize the design of solar greenhouses using MATLAB R2018bThe even-span greenhouse having an ideal height of 3.8 m and a slope of 16°. Modified arc greenhouses have an ellipse ratio of 0.8 for small and large, and 0.25 for medium. Quonset greenhouses, with ellipse ratios of 0.8 for small and 0.6 for medium and large, outperform others in annual solar performance.
A combination of agricultural and energy purposes: Evaluation of a prototype of PV greenhouse tunnel [81]Evaluate the performance of a PV greenhouse prototype for agricultural production and energy generationAnalysis of the indoor microclimate and energy production of a PV greenhouse tunnelThere is a consistency in shading, attributed to the curvilinear shape of the greenhouse, with virtually constant shading within it from March to September during the day. In the other months, shading alternated between the inside and outside of the tunnel greenhouse. With the photovoltaic system used, the shading did not exceed 40% throughout the year.
A comparative study of greenhouse shapes and orientations under the climatic conditions of Marrakech, Morocco [72]Compare the thermal performance of different shapes and orientations of greenhouses in MarrakechComparative analysis using modeling and simulation in MatlabIrregular-span greenhouses with a 12° sloping roof maximize solar capture, surpassing regular-span greenhouses with 17° slopes and ellipticals. The east–west orientation is ideal, especially in Marrakech, to optimize sun exposure in winter, benefiting processes such as drying.
Performance assessment of the integration of semitransparent solar cells with different geometry of greenhouses under different climate regions [51]Optimize energy efficiency and sustainability in greenhouses by integrating organic solar cells(OSCs) photovoltaic energy into energy consumption.Evaluate the energy demand of different types of conventional greenhouses. Examine how the integration of OSCs affects this energy demand and recommend the most efficient design.Flat arches are ideal for dry and cold climates, Sawtooth for tropical, and A-frames for temperate and continental climates. Implementing OSCs can cut energy consumption by 15 to 58%, depending on the location and design of the greenhouse, with electricity generation ranging from
173.7 kWh/m2 to 247.9 MWh/m2.
Table 6. Recent investigations into the influence of coverage/insulation in Agricultural Greenhouses on Energy Performance.
Table 6. Recent investigations into the influence of coverage/insulation in Agricultural Greenhouses on Energy Performance.
Study TitlePurpose of the StudyMethodology UsedMain Results
Estimation of Thermal Performance and Heat Loss in Plastic Greenhouses with and without Thermal Curtains [110]To evaluate the thermal insulation performance of thermal curtains in plastic greenhouses in Korea during winter.Analysis of changes in covering surface temperature and heat transfer coefficients (U-values).It has been shown that using thermal curtains at night in winter results in energy savings of about 28.7%.
Environmental Sustainability of Greenhouse Covering Materials [95]Exploring the Ecological Sustainability of Greenhouse Covering MaterialsSystematic literary review.Roofing materials, including polymers and silica glass, have advantages such as customization and recyclability. Polymers are tunable for functions such as UV protection, while glass is chosen for its beneficial light transmission.
Environmental and nanomechanical testing of an alternative polymer nanocomposite greenhouse covering material [111]To evaluate the environmental and nanomechanical performance of an alternative polymeric nanocomposite greenhouse covering material.Environmental performance tests and nanomechanical analysis.The alternative polymer nanocomposite presents a performance that allows its application for temperature-tolerant crops and crops in colder environments, standing out as a promising option for sustainable greenhouse coverings.
Energy saving techniques for reducing the heating cost of conventional greenhouses [40]Review energy saving techniques to reduce heating costs in greenhousesSystematic literary review.Energy-saving techniques and alternative energy sources for greenhouses are important. The importance of balancing these savings with plants’ agronomic needs and economic viability. An integrated approach can lead to sustainable and energy-efficient operations.
Plant Responses to UV Blocking Greenhouse Covering Materials [94]Review effects of UV-blocking covering materials on plant growthSystematic literary review.UV-blocking materials have mainly positive effects on plant development. These beneficial effects include improvements in vital physiological functions such as photosynthesis and transpiration rate, as well as growth traits.
Performance of novel internal insulation in Chinese solar greenhouse for the cleaner and energy-saving production in high latitudes and cold regions [107]Evaluate internal insulation in solar greenhousesMathematical modeling and experiments13.67% reduction in front cover heat loss, 8.75% total energy savings, 2.01 K average internal temperature increase.
Sustainable Greenhouse Covering Materials with Nano- and Micro-Particle Additives for Enhanced Radiometric and Thermal Properties and Performance [90]Investigate how nano- and microparticle additives incorporated into coating materials can improve the radiometric and thermal properties of these structures.Systematic literary review.Inclusion of nano and microparticles in greenhouse covering materials can significantly improve thermal insulation and light transmission, contributing to more efficient control of the microclimate and enhancing the sustainability and energy efficiency of agricultural operations.
Do Greenhouse Cover Materials Affect Cannabis Performance? [108]Experimental greenhouse study comparing the performance of hemp under five different polyethylene cover films.Numerical simulationThe insertion of PCMs on roofs can be beneficial, reducing energy consumption in a way that depends on the types of PCMs used and the desired internal temperature.
Table 7. Recent Investigations on the Influence of Heating and Cooling Technologies in Agricultural Greenhouses on Energy Performance.
Table 7. Recent Investigations on the Influence of Heating and Cooling Technologies in Agricultural Greenhouses on Energy Performance.
Study TitlePurpose of the StudyMethodology UsedMain Results
Complete greenhouse dynamic simulation tool to assess the crop thermal well-being and energy needs [194]Assess crop thermal well-being and energy needsDetailed dynamic simulation in TRNSYS softwareThe standard simulation accurately reproduces the free-floating thermal response. The detailed simulation indicated energy needs for cooling and heating of 51.4 kWh/m3 and 49.1 kWh/m3, avoiding errors of up to 100%.
Greenhouse design and cooling technologies for sustainable food cultivation in hot climates: Review of current practice and future status [142]Review cooling technologies for hot climatesCritical review and comparison of various technologies.Natural ventilation, evaporative cooling and shading can decrease energy use and improve the environment for crops in hot climates but require effective control in hybrid refrigeration systems.
Recent advances in net-zero energy greenhouses [195]Integrate renewable energy technologiesRenewable Energy Systems AnalysisGeothermal heat can save more than 20% energy and cut operating costs in greenhouses. Solid biomass can provide the necessary heat, cold and C O 2 . Sensitive Thermal Energy Storage (STES) raises the internal temperature and saves up to 28 kWh/m2. Phase change materials in TES systems increase thermal efficiency and reduce energy consumption by 30–40%.
Energy Use in Greenhouses in the EU: A Review Recommending Energy Efficiency Measures and Renewable Energy Sources Adoption [169]Review energy efficiency measures and the adoption of renewable sources in EU GreenhousesLiterature reviewEnergy consumption in EU greenhouses varies and depends mainly on fossil sources. In northern Europe, climate-controlled high-energy greenhouses use energy mainly for heating and cooling. The analysis highlights the need for a standardized methodology to assess energy use in greenhouses to support the transition to sustainable practices.
Comprehensive review on climate control in greenhouses [122]Review highlights greenhouse technologies for hot and arid climates that reduce energy and water use.Literature reviewTechnologies identified to optimize greenhouses in hot climates reduce energy and water consumption, with a focus on efficient control and cooling systems.
Energy savings in greenhouses through the use of heat recovery systems [170]Explore energy savings using heat recovery systemsTransient numerical simulation.Simulation indicated that the heat recovery unit can reduce peak thermal power demand from 263 kW to 84 kW while maintaining the desired humidity and generating energy savings of 45.6% in winter.
Selection criteria of cooling technologies for sustainable greenhouses: A comprehensive review [171]Evaluate and recommend cooling technologies for greenhouses adapted to hot climates.Literature reviewCooling technologies optimize greenhouses for heat, with their effectiveness varying depending on the structure and geographic position. Very hot climates require multiple technologies.
Experimental study of the thermal characteristics of a heat storage wall with micro-heat pipe array (MHPA) and PCM in solar greenhouse [172]Evaluate a greenhouse wall with MHPAs and phase change materials (PCMs) to increase thermal efficiency and support efficient cultivation.Practical experimentation with constructed greenhouses.With micro heat pipe arrays (MHPAs) and PCMs, the experimental wall increased heat storage by 95.35% and heat release by 96.42%, improving the indoor microclimate and benefiting cultivation.
Table 8. Recent Investigations on the Influence of Ventilation in Agricultural Greenhouses on Energy Performance.
Table 8. Recent Investigations on the Influence of Ventilation in Agricultural Greenhouses on Energy Performance.
Study TitlePurpose of the StudyMethodology UsedMain Results
Greenhouse Natural Ventilation Models: How Do We Develop with Chinese Greenhouses? [145]Evaluate natural ventilation models in Chinese greenhouses.Literature reviewVentilation models classified by application scenarios show the influence of variables such as wind direction and greenhouse geometry on the ventilation rate. Methods for measuring ventilation rates validate theoretical models and identify critical wind speeds as determinants of driving force.
Evaluation of airflow pattern and thermal behavior of the arched greenhouses with designed roof ventilation scenarios using CFD simulation [77]Evaluate the performance of natural ventilation in greenhouses and optimize the microclimate to promote sustainable agricultural production.Numerical simulation using CFDIdentification of 85° as the optimal position angle for natural ventilation in arched greenhouses, with results showing the ability of the ventilation scheme to reduce average temperatures by 1.5 °C in 10 min, and reduction in temperature and velocity heterogeneities in 33.3% and 11.89%, respectively.
Study of structural characteristics of wind-speed natural ventilation on single span greenhouse [196]Investigate the influence of natural ventilation on air dynamics and temperature inside greenhouses.Nitrous oxide technique as tracer gas and numerical simulation using CFDVentilation efficiency increases with wind speed and a one-sided ventilation opening is more effective than bilateral openings. Wind direction is crucial to the uniformity of the greenhouse climate.
Dynamic analysis of the natural and mechanical ventilation of a solar greenhouse by coupling controlled mechanical ventilation (CMV) with an earth-to-air heat exchanger (EAHX) [156]Analyze the impact of ventilation systems and EAHX on solar greenhouses, aiming to improve thermal control and energy efficiency.Numerical simulation using TRNsysIn summer, the EAHX system reduced temperature peaks by up to 5 °C, and in winter, it flattened the thermal wave, improving thermal stability. Adequate ventilation is critical to prevent overheating and promote adequate oxygenation for plants.
Study of the effects of vent configuration on mono-span greenhouse ventilation using computational fluid dynamics [73]Analyze the influence of ventilation configuration in greenhouses and propose design solutions to optimize the microclimate, focusing on hot climates.Numerical simulation using CFDSide openings are crucial for ventilation and internal convection, directly affecting humidity and temperature at plant level. Roof ventilation has less influence on the plants’ environment, but is important to avoid stagnant air.
Towards a sustainable greenhouse: Review of trends and emerging practices in analyzing greenhouse ventilation requirements to sustain maximum agricultural yield [143]Evaluate methods to maximize agricultural production in greenhouses in water-scarce regions, and analyze constructive configurationsLiterature review and analysis of emerging trends.The east–west orientation is ideal for optimizing solar radiation in greenhouses, varying with latitude. The quonset shape minimizes, while irregular shapes maximize, incoming solar radiation.
Analysis of Heat and Humidity in Single-Slope Greenhouses with Natural Ventilation [146]Provide quantitative tools for calculating and controlling humidity in greenhouses and support effective ventilation strategies.Analysis of test data on new single voltage greenhouses and estimation of ventilation and humidity using the quality conservation method.The temperature, humidity and C O 2 levels inside, fluctuate due to factors such as planting density and ventilation time. Ventilation affects temperature and humidity, but its power to control is limited. To maintain an ideal balance of these elements, it is crucial to implement effective ventilation strategies.
Table 9. Recent Investigations on the Influence of Shading Technologies in Agricultural Greenhouses on Energy Performance.
Table 9. Recent Investigations on the Influence of Shading Technologies in Agricultural Greenhouses on Energy Performance.
Study TitlePurpose of the StudyMethodology UsedMain Results
Hybrid and Organic Photovoltaics for Greenhouse Applications [136]Exploring the potential of new PV technologies for shading and energy production in greenhousesLiterature review, analysis of simulation studies and experimental workThe potential of innovative PV technologies due to the possibility of adjusting their spectral characteristics to optimize the use of solar energy and create suitable conditions for crop growth, especially in hot and tropical regions.
A Photovoltaic Greenhouse with Variable Shading for the Optimization of Agricultural and Energy Production [135]Present the potential of photovoltaic greenhouses with variable shading to optimize agricultural and energy productionAnalysis of solar radiation throughout the year, considering clear and partially cloudy skiesIt showed that the variation in shading allowed the regulation of internal radiation, choosing the minimum value of radiation required, compatible with the needs of the cultivated plant species.
Testing Organic Photovoltaic Modules for Application as Greenhouse Cover or Shading Element [197]Examining the feasibility of semi-transparent organic photovoltaic modules as shading materialMeasurement of radiometric and thermal properties of OPV modules under different angles of incidenceIt concluded that OPV modules are suitable for shading and generating electricity in greenhouses, but are currently expensive and have a relatively short life span.
Climate Assessment of Greenhouse Equipped with South-Oriented PV Roofs [198]Understanding the effect of shading induced by south-facing photovoltaic panels on the greenhouse’s internal climateExperimental and numerical simulation study using CFDIt found that greenhouses equipped with photovoltaic panels provide more favorable climate conditions during the summer season, which can balance crop production and PV electricity production.
Dye Sensitized Solar Cell (DSSC) greenhouse shading: New insights for solar radiation manipulation [132]Evaluate the effectiveness of DSSC as a sustainable alternative in greenhouses, and explore its benefits for crop growth and simultaneous electrical production.Literature review and analysis of photovoltaic technologies.DSSCs are economical, flexible and efficient in low light conditions, outperforming silicon solar cells. DSSC allows the use of light for photosynthesis while generating electricity, and performs well at high temperatures.
The effect of different levels of shading in a photovoltaic greenhouse with a north–south orientation [134]Identify the limit of shading by photovoltaic panels in greenhouses that do not harm the yield of tomato crops.Numerical simulation study using CFDThe use of photovoltaic panels in greenhouses can reduce internal solar radiation, affecting the yield and quality of tomatoes. Shades above 15% negatively impact fruit color, and shading greater than 30% harms overall quality, without affecting pH.
Smart and solar greenhouse covers: Recent developments and future perspectives [133]Examine smart and solar materials covering greenhousesLiterature analysis and case studiesThe implementation of smart roofing materials, especially semi-transparent photovoltaics, presents significant economic and energy advantages for greenhouses, although commercializing these innovations remains a challenge.
Table 10. Recent investigations on the advances and applications of innovative technologies in agricultural greenhouses on Energy Performance.
Table 10. Recent investigations on the advances and applications of innovative technologies in agricultural greenhouses on Energy Performance.
Study TitlePurpose of the StudyMethodology UsedMain Results
Energy-efficient operation and modeling for greenhouses: A literature review [229]Discuss strategies to improve energy efficiency in greenhousesLiterature reviewIt highlights the importance of controlling environmental parameters, using sensor networks and implementing advanced control algorithms. The review covers various modeling and operating techniques that aim to reduce energy consumption while maintaining or improving plant growing conditions.
Comparison of finite element and finite volume methods for simulation of natural ventilation in greenhouses [256]Evaluate the efficiency and accuracy of two different discretization methods (MEF and MVF). Used as solvers in CFD simulations.Numerical simulation model using the software FVM ANSYS/
FLUENT v 6.3 and ANSYS/FLOTRAN v. 11.0
MVF is more commonly used, presenting lower computational requirements compared to MEF, which requires more computing time and memory. The research emphasized the importance of properly choosing experimental data, such as temperature distributions and velocity vector measurements, for effective validation of CFD models.
Experimental and numerical investigation of the thermal performance of evaporatively cooled greenhouses in hot and arid climates [248]Analyze the thermal performance of evaporatively cooled greenhouses operating in Qatar, considering design factors such as greenhouse geometry, operational parameters and geographic location.Experiments and Numerical Simulation using CFDFans located at or below crop height were effective in decreasing the average temperature inside the greenhouse. Doubling the ventilation rate from 20 ACH to 40 ACH resulted in a further reduction in greenhouse air temperature. Increasing ventilation rates reduced temperature rise due to high incoming solar radiation and the irregular shape of the greenhouse roof resulted in the lowest average internal temperature.
Geothermal energy potential for cooling/heating greenhouses in hot arid regions [11]Explore the use of geothermal energy in greenhouses, specifically for cooling and heating applications in arid regions.Annual measurement of underground temperature, assessment of condensation in EAHE pipes and quantification of cooling/heating capacity.A depth of 3 m is ideal for burying EAHE pipes, with soil temperatures of 32 °C in summer and 29 °C in winter, providing maximum cooling/heating capacities of 1000/890 MJ per day for every 1 m3 of moist air exhausted from a greenhouse, without condensation of water vapor in the tubes during the cooling process.
Cooling improvement of an agricultural greenhouse using geothermal energy in a desert climate [157]Evaluate the effectiveness of using geothermal energy to cool agricultural greenhouses in desert climates, specifically in the Ouargla region, Algeria.Combination of an energy audit with Trnsys, CFD simulations with AnsysApplication of EAHE can significantly reduce the internal temperatures of greenhouses, making it a sustainable and efficient solution to face the challenges of growing in extreme conditions. The effectiveness of the geothermal cooling system is validated by both numerical and experimental approaches, highlighting its potential to improve agricultural productivity in hot regions.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Castro, R.P.; Dinho da Silva, P.; Pires, L.C.C. Advances in Solutions to Improve the Energy Performance of Agricultural Greenhouses: A Comprehensive Review. Appl. Sci. 2024, 14, 6158. https://doi.org/10.3390/app14146158

AMA Style

Castro RP, Dinho da Silva P, Pires LCC. Advances in Solutions to Improve the Energy Performance of Agricultural Greenhouses: A Comprehensive Review. Applied Sciences. 2024; 14(14):6158. https://doi.org/10.3390/app14146158

Chicago/Turabian Style

Castro, Rodrigues Pascoal, Pedro Dinho da Silva, and Luís Carlos Carvalho Pires. 2024. "Advances in Solutions to Improve the Energy Performance of Agricultural Greenhouses: A Comprehensive Review" Applied Sciences 14, no. 14: 6158. https://doi.org/10.3390/app14146158

APA Style

Castro, R. P., Dinho da Silva, P., & Pires, L. C. C. (2024). Advances in Solutions to Improve the Energy Performance of Agricultural Greenhouses: A Comprehensive Review. Applied Sciences, 14(14), 6158. https://doi.org/10.3390/app14146158

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop