Search Results (122)

Controlled environment agriculture (CEA) pivots food production from an outdoor field setting to the indoors where growing conditions can be calibrated to fit crop needs. This research investigates vertical farms as a type of CEA. In particular, using the St. Louis area as a case study, it provides data-driven support for optimizing a vertical farm’s business model including its supply chain. The methodology presented here informs agri-preneurs about what crops to grow in a vertical farm, how much to grow given local market demand, and what vertical farm configuration (e.g., Dutch bucket, nutrient film technique, deep water culture) a facility should use. Based on the case study’s base scenario, the simulated vertical farm business would record an economic loss. However, the study did find several paths to improving profitability. First, reducing fixed and variable costs benefits profitability. Proper facility-level production and resource planning helps with managing the fixed costs. Second, increasing market prices may benefit profitability, but it has diminishing returns. As a result, firms can justify making investments that enhance their reputation and market competitiveness, though the advantage these marketing activities provide will decline as prices increase. Third, growing demand or increasing market share does not necessarily improve profitability. Full article

A greenhouse with properly managed temperature can provide 5 to 10 times greater yield than conventional methods for crops such as blueberries, cucumbers, and tomatoes; the yield is also of higher quality. However, existing designs in Australia often follow practices developed for cooler regions, making them less effective under local high-radiation conditions. To determine the design parameters for the local condition, this study develops and validates a numerical model of a commercial blueberry greenhouse, applying it to examine how structural parameters, including overall height, arch height, and number of spans, influence indoor temperature distribution in multi-span hoop structures. Results show that increasing greenhouse height by 0.40 m reduced average temperature by up to 0.62%, whereas raising arch height by the same increment led to a marginal increase of 0.15%. In contrast, expanding span numbers from 2 to 12 resulted in a maximum temperature difference of 6 °C (approximately 20% above ambient temperature) across the structure, posing significant risks to plant growth. These findings provide a theoretical basis for optimising design parameters that minimise heat stress while reducing reliance on fossil-fuel-based cooling. The study highlights how tailoring greenhouse design to local conditions can improve productivity and support both environmental and economic sustainability. Full article

According to United Nations projections, the global population is expected to reach 9.7 billion by 2050, with 70% residing in urban areas, while arable land availability continues to decline. Vertical farming (VF) offers a promising pathway for sustainable urban food production by utilizing vertical space and controlled environments. Among emerging approaches, the adaptive vertical farm (AVF) introduces movable shelving systems that adjust to plant growth stages, allowing a higher number of cultivation shelves to be accommodated within the same rack height. In this study, we developed a computational model to quantify and compare the energy consumption of AVF and conventional VF systems under industrial-scale conditions. The reference scenario considered 272 multilevel racks, each hosting 8 shelves in the VF and 15 shelves in the AVF, with Lactuca sativa as the test crop. Energy consumption for thermohygrometric control and lighting was estimated under different sowing schedules, with crop growth dynamics simulated using scheduling algorithms. Plant heat loads were calculated through the Penman–Monteith model, enabling a robust estimation of evapotranspiration and its impact on indoor climate control. Simulation results show that the AVF achieves an average 22% reduction in specific energy consumption for climate control compared to the VF, independently of sowing strategies. Moreover, the AVF nearly doubles the number of cultivation shelves within the same footprint, increasing the cultivable surface area by over 400% compared to traditional flat indoor systems. This work provides the first quantitative assessment of AVF energy performance, demonstrating its potential to simultaneously improve land-use efficiency and reduce energy intensity, thereby supporting the sustainable integration of vertical farming in urban food systems. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

Urban agriculture (UA) is emerging as a promising strategy for sustainable food production in response to growing environmental pressures. Indoor vertical farming (IVF), combining Controlled Environment Agriculture (CEA) with Building-Integrated Agriculture (BIA), enables efficient resource use and year-round crop cultivation in urban settings. This study assesses the environmental performance of a prospective IVF system located on a university campus in Portugal, focusing on the integration of photovoltaic (PV) energy as an alternative to the conventional electricity grid (GM). A Life Cycle Assessment (LCA) was conducted using the Environmental Footprint (EF) method and the LANCA model to account for land use and soil-related impacts. The PV-powered system demonstrated lower overall environmental impacts, with notable reductions across most impact categories, but important trade-offs with decreased soil quality. The LANCA results highlighted cultivation and packaging as key contributors to land occupation and transformation, while also revealing trade-offs associated with upstream material demands. By combining EF and LANCA, the study shows that IVF systems that are not soil-based can still impact soil quality indirectly. These findings contribute to a broader understanding of sustainability in urban farming and underscore the importance of multi-dimensional assessment approaches when evaluating emerging agricultural technologies. Full article

Indoor farming presents a sustainable response to urbanization and climate change, yet optimizing light use efficiency (LUE) remains vital for maximizing crop yield and minimizing energy use. This study introduces an IoT-based framework for adaptive light management in controlled environments, using lettuce (Lactuca sativa L.) as a model crop due to its rapid growth and sensitivity to light spectra. The system integrates advanced LED lighting, real-time sensors, and cloud-based analytics to enhance light distribution and automate adjustments based on growth stages. The key findings indicate a 20% increase in energy efficiency and a 15% improvement in lettuce growth compared to traditional static models. Novel metrics—Light Use Efficiency at Growth stage Canopy Level (LUEP) and Lamp Level (LUEL)—were developed to assess system performance comprehensively. Simulations identified optimal growth conditions, including a light intensity of 350–400 µmol/m²/s and photoperiods of 16–17 h/day. Spectral optimization showed that a balanced blue-red light mix benefits vegetative growth, while higher red content supports flowering. The framework’s feedback control ensures rapid (<2 s) and accurate (>97%) adjustments to environmental deviations, maintaining ideal conditions throughout growth stages. Comparative analysis confirms the adaptive system’s superiority over static models in responding to dynamic environmental conditions and improving performance metrics like LUEP and LUEL. Practical recommendations include stage-specific guidelines for light spectrum, intensity, and duration to enhance both energy efficiency and crop productivity. While tailored to lettuce, the modular system design allows for adaptation to a variety of leafy greens and other crops with species-specific calibration. This research demonstrates the potential of IoT-driven adaptive lighting systems to advance precision agriculture in indoor environments, offering scalable, energy-efficient solutions for sustainable food production. Full article

In the current era of agricultural management practices, known as agricultural 5.0, optimal indoor environments are associated with comfortable temperatures, regulated humidity, and good air quality—essential variables to improve yields. Given this scenario, there is a need for innovative ecosystems that automate indoor environmental monitoring in an affordable and scalable way. This paper presents the scope of the development and validation of an IoT-based ecosystem designed to monitor and control environmental conditions in agricultural barns. The objective is to present a cost-effective and easily accessible environmental monitoring system for barn buildings and agricultural storage areas, promoting the welfare of animals, humans, and crops, and contributing to the sustainable development of the agricultural industry. The system integrates wireless sensors, predictive algorithms, a web interface and cloud infrastructure to optimize temperature and humidity. A proof-of-concept assessment was performed to determine whether the modular architecture offers scalability, while the responsive web interface ensures cross-device accessibility. The results show data accuracy above 95%, prediction efficiency of 96%, and increases in production yields. This solution demonstrates economic and operational advantages over existing technologies, promoting sustainability and automation in agricultural management practices in hangars and barns, in alignment with the United Nations’ Sustainable Development Goals (SDGs). Full article

In a plant factory, maintaining proper and uniform air/moisture movement above the crop canopy is crucial for aiding plant growth. This research has utilized a three-dimensional computation model to investigate airflow and heat transfer in a plant factory, where airflow, heat, and humidity distributions above plant crops were calculated concerning five categories of crop planting density (P_d) and air change rate (ACH) in the crop area. Spatial uniformities of airflow velocity, temperature, and relative humidity immediately above the crops are evaluated using the objective uniformity parameter (OU), relative standard deviation of temperature (RSD_T) and relative standard deviation of relative humidity (RSD_RH), respectively. Furthermore, a factor of effectiveness (θ) is defined, depending on the uniformity of velocity, temperature, and relative humidity distribution, to comprehensively evaluate the impact of various ACH with P_d on overall effectiveness. Full numerical results show that air velocity, temperature, and relative humidity above the crops are notably influenced by P_d and ACH. As ACH increases, the OU of the air above the indoor crop also expands. Moreover, higher OU values are observed for smaller crop P_d. However, excessively small crop area planting densities and excessively large ACH do not result in a higher OU for the air above the crop. As ACH increases, both RSD_T and RSD_RH decay for the whole range of crop P_d. Moreover, smaller P_d values could achieve the uniformity of thermal fields, while having minimal effects on the relative humidity distributions. Generally, increasing ACH and decreasing P_d could enhance overall value of θ. However, excessively increasing ACH and decreasing P_d does not have a significant effect on θ, which is jointly influenced by OU, RSD_T, and RSD_RH. Therefore, a more suitable combination of ACH and P_d is urgently required to improve the design of agricultural system to enhance crop microclimate uniformity for optimal plant growth and productivity. Full article

Artificial lighting is essential in indoor agriculture, directly influencing plant growth and productivity. Optimizing its use requires advanced technologies that improve light management and adaptation to crop needs. This systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, examines recent advancements in artificial lighting technologies, focusing on their applications, challenges, and future directions. A systematic search in Web of Science (WOS) and Scopus identified 70 relevant studies published between 2019 and 2024. The analysis highlights five major technology groups: (i) lighting control systems, with Light-Emitting Diodes (LEDs) as the dominant solution; (ii) Internet of Things (IoT) incorporating sensors, deep neural networks, Artificial Intelligence (AI), digital twins, and machine learning (ML) for real-time optimization, as well as communication technologies, enabling remote control and data-driven adjustments; (iii) simulation and modeling tools, refining lighting strategies to enhance plant responses and system performance; and (iv) complementary energy sources, improving lighting sustainability. IoT-driven automation has significantly improved artificial lighting efficiency, optimizing adaptation and plant-specific management. However, challenges such as system complexity, high energy demands, and scalability limitations persist. Future research should focus on refining IoT-driven adaptive lighting, improving sensor calibration for precise real-time adjustments, and developing cost-effective modular systems to enhance widespread adoption and optimize resource use. Full article

The integration of cloud computing, IoT (Internet of Things), and artificial intelligence (AI) is transforming precision agriculture by enabling real-time monitoring, data analytics, and dynamic control of environmental factors. This study develops a cloud-driven data analytics pipeline for indoor agriculture, using lettuce as a test crop due to its suitability for controlled environments. Built with Apache NiFi (Niagara Files), the pipeline facilitates real-time ingestion, processing, and storage of IoT sensor data measuring light, moisture, and nutrient levels. Machine learning models, including SVM (Support Vector Machine), Gradient Boosting, and DNN (Deep Neural Networks), analyzed 12 weeks of sensor data to predict growth trends and optimize thresholds. Random Forest analysis identified light intensity as the most influential factor (importance: 0.7), while multivariate regression highlighted phosphorus (0.54) and temperature (0.23) as key contributors to plant growth. Nitrogen exhibited a strong positive correlation (0.85) with growth, whereas excessive moisture (–0.78) and slightly elevated temperatures (–0.24) negatively impacted plant development. To enhance resource efficiency, this study introduces the Integrated Agricultural Efficiency Metric (IAEM), a novel framework that synthesizes key factors, including resource usage, alert accuracy, data latency, and cloud availability, leading to a 32% improvement in resource efficiency. Unlike traditional productivity metrics, IAEM incorporates real-time data processing and cloud infrastructure to address the specific demands of modern indoor farming. The combined approach of scalable ETL (Extract, Transform, Load) pipelines with predictive analytics reduced light use by 25%, water by 30%, and nutrients by 40% while simultaneously improving crop productivity and sustainability. These findings underscore the transformative potential of integrating IoT, AI, and cloud-based analytics in precision agriculture, paving the way for more resource-efficient and sustainable farming practices. Full article

Food systems around the world are challenged to meet increased demand while also mitigating ecosystem pressures from their current structure. Controlled environment agriculture (CEA) offers a potential solution to augment the food supply by adopting innovative production systems designed to overcome environmental resource limitations and efficiently serve densely populated urban areas. By utilizing Elkington’s profit, plant, and people framework (3Ps), this article assesses the sustainability of a major subcategory of CEA farms: indoor agriculture vertical farms (IA/VFs). The qualitative analysis attempts to answer the question of whether IA/VFs have fulfilled their potential. Results suggest that IA/VFs have not yet optimized their positive impact on future food system sustainability. For each of the three Ps, IA/VF’s observed progress and required breakthroughs are summarized. Notably, the financial viability of an IA/VF is more likely to be achieved through whole systems solutions: growing the right crops in the right environment, efficient use of resources, and effective consumer targeting. Significant progress is being made in the direction of innovating IA/VF’s role in future food systems. Through public–private partnerships and further analyses, further progress can be made toward realizing IA/VF’s potential to address the growing demands of an expanding world population and shrinking resource base. Full article

This paper examines the essential role of artificial lighting in protected agriculture, a crucial sector in addressing the increasing global food demand and the challenges posed by climate change. It explores how advanced lighting technologies, particularly LED systems, have revolutionized productivity and sustainability in greenhouses and indoor or urban farming systems. These technologies enable precise control over key factors influencing crop growth, optimizing both yield and resource efficiency. The methodology was based on a bibliometric analysis developed in four phases: collection of information in the scientific database Scopus, filtering and selection of relevant documents, quantitative and qualitative analysis of trends, and visualization of the results using tools such as VOSviewer. The study included scientific publications between 1974 and 2024, focusing on keywords related to greenhouse lighting technologies and protected agriculture systems. Key findings identified a significant increase in research over the last two decades, with countries such as the United States, Canada, the Netherlands, and China leading the way in scientific output. The main trends in artificial lighting for protected agriculture include the use of specific light spectra (particularly red and blue) to optimize photosynthesis and morphogenesis, as well as the integration of LED systems with digital sensors and controllers for enhanced precision. However, in developing countries such as Colombia, the adoption of these technologies remains in its early stages, presenting significant opportunities for implementation and expansion. Additionally, this bibliometric analysis provides a robust foundation for identifying key areas for improvement and guiding future research toward more sustainable and efficient agricultural practices. Full article

Cannabis (Cannabis sativa L.) is one of the earliest cultivated crops and is valued for its medicinal compounds, food, fibre, and bioactive secondary metabolites. The rapid expansion of the cannabis industry has surpassed the development of production system knowledge. The scientific community currently focuses on optimising agronomic and environmental factors to enhance cannabis yield and quality. However, cultivators face significant challenges from severe pathogens, with limited effective control options. The principal diseases include root rot, wilt, bud rot, powdery mildew, cannabis stunt disease, and microorganisms that reduce post-harvest quality. Sustainable management strategies involve utilising clean planting stocks, modifying environmental conditions, implementing sanitation, applying fungal and bacterial biological control agents, and drawing on decades of research on other crops. Plant–microbe interactions can promote growth and regulate secondary metabolite production. This review examines the recent literature on pathogen management in indoor cannabis production using biocontrol agents. Specific morphological, biochemical, and agronomic characteristics hinder the implementation of biological control strategies for cannabis. Subsequent investigations should focus on elucidating the plant–microbe interactions essential for optimising the effectiveness of biological control methodologies in cannabis cultivation systems. Full article

Innovation in cultivation methods is essential to address the growing challenges in agriculture, including abiotic and biotic stress, soil degradation, and climate change. Aeroponics, a particular type of hydroponics, presents a promising solution by improving yield and resource use efficiency, especially in controlled environments such as plant factories with artificial lighting (PFALs). Additionally, non-thermal plasma (NTP), a partially ionized gas containing reactive oxygen and nitrogen species, can affect plant development and physiology, further enhancing crop production. The objective of this study was to explore the potential of NTP as an innovative method to enhance crop production by treating the nutrient solution in aeroponic systems. During this study, three experiments were conducted to assess the effects of NTP-treated nutrient solutions on baby leaf lettuce (Lactuca sativa L.) aeroponically grown indoors. The nutrient solution was treated with ionized air in a treatment column separated from the aeroponic system by making the ionized air bubble from the bottom of the column. After 2 min of NTP application, a pump took the nutrient solution from the treatment column and sprayed it on the roots of plants. Various frequencies of NTP application were tested, ranging from 2.5% to 50% of irrigation events with nutrient solution activated with NTP. Results indicated that low-frequency NTP treatments (up to 5% of irrigations) stimulated plant growth, increasing leaf biomass (+18–19%) and enhancing the concentration of flavonoids (+16–18%), phenols (+20–21%), and antioxidant capacity (+29–53%). However, higher NTP frequencies (25% and above) negatively impacted plant growth, reducing fresh and dry weight and root biomass, likely due to excessive oxidative stress. The study demonstrates the potential of NTP as a tool for improving crop quality and yields in aeroponic cultivation, with optimal benefits achieved at lower treatment frequencies. Full article

Controlled Environment Agriculture (CEA) offers a viable solution for sustainable crop production, yet the optimization of the latter requires precise modeling and resource management. This study introduces a novel hybrid plant growth model integrating stochastic, empirical, and optimization approaches, using Internet of Things sensors for real-time data collection. Unlike traditional methods, the hybrid model systematically captures environmental variability, simulates plant growth dynamics, and optimizes resource inputs. The prototype growth chamber, equipped with IoT sensors for monitoring environmental parameters such as light intensity, temperature, CO₂, humidity, and water intake, was primarily used to provide accurate input data for the model and specifically light intensity, water intake and nutrient intake. While experimental tests on lettuce were conducted to validate initial environmental conditions, this study was focused on simulation-based analysis. Specific tests simulated plant responses to varying levels of light, water, and nutrients, enabling the validation of the proposed hybrid model. We varied light durations between 6 and 14 h/day, watering levels between 5 and 10 L/day, and nutrient concentrations between 3 and 11 g/day. Additional simulations modeled different sowing intervals to capture internal plant variability. The results demonstrated that the optimal growth conditions were 14 h/day of light, 9 L/day of water, and 5 g/day of nutrients; maximized plant biomass (200 g), leaf area (800 cm²), and height (90 cm). Key novel metrics developed in this study, the Growth Efficiency Ratio (GER) and Plant Growth Index (PGI), provided solid tools for evaluating plant performance and resource efficiency. Simulations showed that GER peaked at 0.6 for approximately 200 units of combined inputs, beyond which diminishing returns were observed. PGI increased to 0.8 to day 20 and saturated to 1 by day 30. The role of IoT sensors was critical in enhancing model accuracy and replicability by supplying real-time data on environmental variability. The hybrid model’s adaptability in the future may offer scalability to diverse crop types and environmental settings, establishing a foundation for its integration into decision-support systems for large-scale indoor farming. Full article