Search Results (14)

Rapid urbanization, climate variability, and land degradation are increasingly challenging traditional open-field farming systems. Soilless farming (SLF) has emerged as a complementary approach to enhance horticultural resilience in space-constrained and climate-stressed environments. This review critically evaluates the role of SLF within the broader framework of climate-smart agriculture (C-SA), with a particular focus on its applications in urban and peri-urban settings. Drawing on a systematic review of the existing literature, the study explores how SLF technologies contribute to efficient resource use, localized food production, and environmental sustainability. By decoupling crop cultivation from soil, SLF enables precise control over nutrient delivery and water use in enclosed environments, such as vertical farms, greenhouses, and container-based units. These systems offer notable advantages regarding water conservation, increased yield per unit area, and adaptability to non-arable or degraded land, making them particularly relevant for high-density cities, arid zones, and climate-sensitive regions. SLF systems are categorized into substrate-based (e.g., coco peat and rock wool) and water-based systems (e.g., hydroponics, aquaponics, and aeroponics), each with distinct design requirements, nutrient management strategies, and crop compatibility. Emerging technologies—including artificial intelligence, the Internet of Things, and automation—further enhance SLF system efficiency through real-time data monitoring and precision control. Despite these advancements, challenges remain. High setup costs, energy demands, and the need for technical expertise continue to limit large-scale adoption. While SLF is not a replacement for traditional agriculture, it offers a strategic supplement to bolster localized food systems and address climate-related risks in horticultural production. Urban horticulture is no longer a peripheral activity; it is becoming an integral element of sustainable urban development. SLF should be embedded within broader resilience strategies, tailored to specific socioeconomic and environmental contexts. Full article

The accurate prediction of sewage treatment indicators is crucial for optimizing management and supporting sustainable water use. This study proposes the KAN-LSTM model, a hybrid deep learning model combining Long short-term memory (LSTM) networks, Kolmogorov-Arnold Network (KAN) layers, and multi-head attention. The model effectively captures complex temporal dynamics and nonlinear relationships in sewage data, outperforming conventional methods. We applied correlation analysis with time-lag consideration to select key indicators. The KAN-LSTM model then processes them through LSTM layers for sequential dependencies, KAN layers for enhanced nonlinear modeling via learnable B-spline transformations, and multi-head attention for dynamic weighting of temporal features. This combination handles short-term patterns and long-range dependencies effectively. Experiments showed the model’s superior performance, achieving 95.13% R-squared score for FOss (final sedimentation basin outflow suspended solid, one indicator of our research predictions)and significantly improving prediction accuracy. These advancements in intelligent sewage treatment prediction modeling not only enhance water sustainability but also demonstrate the transformative potential of hybrid deep learning approaches. This methodology could be extended to optimize predictive tasks in sustainable aquaponic systems and other smart aquaculture applications. Full article

Hydroponics is a soilless farming technique that has emerged as a sustainable alternative. However, new technologies such as Industry 4.0, the internet of things (IoT), and artificial intelligence are needed to keep up with issues related to economics, automation, and social challenges in hydroponics farming. One significant issue is optimizing growth parameters to identify the best conditions for growing fruits and vegetables. These parameters include pH, total dissolved solids (TDS), electrical conductivity (EC), light intensity, daily light integral (DLI), and nutrient solution/ambient temperature and humidity. To address these challenges, a systematic literature review was conducted aiming to answer research questions regarding the optimal growth parameters for leafy green vegetables and herbs and spices grown in hydroponic systems. The review selected a total of 131 papers related to indoor farming, hydroponics, and aquaponics. The review selected a total of 123 papers related to indoor farming, hydroponics, and aquaponics. The majority of the articles focused on technology description (38.5%), artificial illumination (26.2%), and nutrient solution composition/parameters (13.8%). Additionally, remaining 10.7% articles focused on the application of sensors, slope, environment and economy. This comprehensive review provides valuable information on optimized growth parameters for smart hydroponic systems and explores future prospects and the application of digital technologies in this field. Full article

As the demand for high-quality food rises, especially amidst the COVID-19 pandemic and the continuous development of society meaning that people demand to eat well, ensuring food security has become increasingly urgent. Agricultural technology is evolving, with aquaponic systems emerging as a promising solution to urban food needs. However, these systems present challenges, such as maintaining optimal water quality and minimizing environmental control errors. In this study, we propose a comprehensive approach combining a literature review and controlled experiments. Through the literature review, the recent findings on water management and sustainability in food production were analyzed, providing crucial insights for enhancing aquaponic system performance. Building on this, a series of experiments were conducted to develop and test a water quality management system using PID control. The integration of PID control showed good performance and reduced errors in SIMULINK, and we applied three controls to manage the stability and responsiveness of the aquaponic system. The optimal values obtained from the controller of the vegetable tank system were 4,706,691,503 and −174.418; for the fish tank, they were 36,167, 0.00126, and −174.418; and for the heater system, they were 4.761, 0.0488, and −31.88. This solution is expected to be responsive and provide stable control over various variables. Full article

Integration of the Internet of Things (IoT) into the fields of wastewater treatment and water quality prediction has the potential to revolutionize traditional approaches and address urgent challenges, considering the global demand for clean water and sustainable systems. This comprehensive article explores the transformative applications of smart IoT technologies, including artificial intelligence (AI) and machine learning (ML) models, in these areas. A successful example is the implementation of an IoT-based automated water quality monitoring system that utilizes cloud computing and ML methods to effectively address the above-mentioned issues. The IoT has been employed to optimize, simulate, and automate various aspects, such as monitoring and managing natural systems, water-treatment processes, wastewater-treatment applications, and water-related agricultural practices like hydroponics and aquaponics. This review presents a collection of significant water-based applications, which have been combined with the IoT, artificial neural networks, or ML and have undergone critical peer-reviewed assessment. These applications encompass chlorination, adsorption, membrane filtration, monitoring water quality indices, modeling water quality parameters, monitoring river levels, and automating/monitoring effluent wastewater treatment in aquaculture systems. Additionally, this review provides an overview of the IoT and discusses potential future applications, along with examples of how their algorithms have been utilized to evaluate the quality of treated water in diverse aquatic environments. Full article

Aquaponics is an emerging area of agricultural sciences that combines aquaculture and hydroponics in a symbiotic way to enhance crop production. A stable smart aquaponic system requires estimating the fish size in real time. Though deep learning has shown promise in the context of smart aquaponics, most smart systems are extremely slow and costly and cannot be deployed on a large scale. Therefore, we design and present a novel neuromorphic computer that uses spiking neural networks (SNNs) for estimating not only the length but also the weight of the fish. To train the SNN, we present a novel hybrid scheme in which some of the neural layers are trained using direct SNN backpropagation, while others are trained using standard backpropagation. By doing this, a blend of high hardware efficiency and accuracy can be achieved. The proposed computer SpikoPoniC can classify more than 84 million fish samples in a second, achieving a speedup of at least 3369× over traditional general-purpose computers. The SpikoPoniC consumes less than 1100 slice registers on Virtex 6 and is much cheaper than most SNN-based hardware systems. To the best of our knowledge, this is the first SNN-based neuromorphic system that performs smart real-time aquaponic monitoring. Full article

In the next twenty years, the scarcity of food shortage and drinking water will appear in Egypt due to the growth of industries and agriculture. This paper develops a conceptual design of the new technologies in the field of water–energy–food in new cities. Border lines are the internal relationship, external influence, and linkage system evaluation for WEF nexus. The major problems of using fossil energy in desalination are emissions and non-renewability, as well as the preference for dispersed freshwater production instead of concentrated output. The design of a desalination system that is integrated with renewable energies is critical these days. This type of system can also reduce the production of environmental pollutants due to reduced energy consumption and transfer of freshwater. GIS data from the United Nations have confirmed the existence of an underground reservoir in Wadi-Dara that can cultivate 1000 acres using smart farming techniques to reach a circular economy for an integrated solution between the water–energy nexus. The possibility of cultivating a hundred acres in Wadi-Dara on the Red Sea coast exists, through which about one million people could be settled. In this comprehensive review, we conducted a deep study in order to establish a sustainable integrated lifestyle in the Dara Valley region in terms of the availability of potable water, clean energy, and agriculture. Sustainable integrated solutions were conducted for seawater desalination using beach sand filtration wells as a pretreatment for seawater using renewable energy, e.g., wind energy (18% wind turbines), and photovoltaic panels (77% PV panels). Strategic food will be cultivated using smart farming that includes an open ponds cultivation system of microalgal cells to synthesis (5.0% of bio-fuel (. Aqua agriculture and aquaponics will cultivate marine culture and integrate mangrove, a shrimp aquaculture. A municipal waste water treatment is conceived for the irrigation of shrubby forests and landscapes. Mixotrophic cultures were explored to achieve a sustained ecological balance. Food, poultry and animal waste management, as well as a cooker factory, were included in the overall design. The environmental impact assessment (EIA) study shows a low risk due to anticipated net zero emissions, a 75% green city, and optimal waste recycling. This research assists in combining research efforts to address the challenging processes in nexus research and build resilient and sustainable water, energy, and food systems. Full article

Heavy metal concentrations that must be maintained in aquaponic environments for plant growth have been a source of concern for many decades, as they cannot be completely eliminated in a commercial set-up. Our goal was to create a low-cost real-time smart sensing and actuation system for controlling heavy metal concentrations in aquaponic solutions. Our solution entails sensing the nutrient concentrations in the hydroponic solution, specifically calcium, sulfate, and phosphate, and sending them to a Machine Learning (ML) model hosted on an Android application. The ML algorithm used in this case was a Linear Support Vector Machine (Linear-SVM) trained on top three nutrient predictors chosen after applying a pipeline of Feature Selection methods namely a pairwise correlation matrix, ExtraTreesClassifier and Xgboost classifier on a dataset recorded from three aquaponic farms from South-East Texas. The ML algorithm was then hosted on a cloud platform which would then output the maximum tolerable levels of iron, copper and zinc in real time using the concentration of phosphorus, calcium and sulfur as inputs and would be controlled using an array of dispensing and detecting equipments in a closed loop system. Full article

According to the “World Population Prospects 2022” released by the United Nations in August 2022, the world will officially enter an “aging society”. In order to provide the elderly with an improved quality of daily life, “health promotion” and “prevention of disease” will be important. With respect to care of the elderly, the concepts of “therapeutic environment” and “green care” have been explored and developed. Therefore, in this study, we combine the currently popular Internet of Things (IoT) into an aquaponics system and proposes a smart green care system (SGCS). The proposed system uses face recognition technology to record the labor and rehabilitation history of the elderly, in combination with environmental data analysis, to enable automatic control decisions for equipment in conjunction with a voice control system to reduce the obstacles faced by the elderly in operating the information system. It also uses image recognition technology to monitor and notify about plant diseases and insect pests to achieve automatic management and enhance the interaction between the elderly and the SGCS through human–computer interaction. The SGCS allows the elderly to guide it to participate in appropriate activities through direct contact with the natural environment, thereby enhancing the quality of green healing life. In this study, taking long-term care institutions as an example, we verified proof of concept (PoC), proof of service (PoS), and proof of business (PoB), confirming the feasibility of the SGCS. The SGCS proposed in this study can be successfully used in long-term care institutions and various other environments, such as medical units and home care contexts. It can take full advantage of the functions associated with the concept of “healing environment” and “green care” widely recognized by users. Therefore, it can be widely used in the field of long-term care in the future. Full article

The use of automation, Internet-of-Things (IoT), and smart technologies is being rapidly introduced into the development of agriculture. Technologies such as sensing, remote monitoring, and predictive tools have been used with the purpose of enhancing agriculture processes, aquaponics among them, and improving the quality of the products. Digital twinning enables the testing and implementing of improvements in the physical component through the implementation of computational tools in a ‘twin’ virtual environment. This paper presents a framework for the development of a digital twin for an aquaponic system. This framework is validated by developing a digital twin for the grow beds of an aquaponics system for real-time monitoring parameters, namely pH, electroconductivity, water temperature, relative humidity, air temperature, and light intensity, and supports the use of artificial intelligent techniques to, for example, predict the growth rate and fresh weight of the growing crops. The digital twin presented is based on IoT technology, databases, a centralized control of the system, and a virtual interface that allows users to have feedback control of the system while visualizing the state of the aquaponic system in real time. Full article

Aquaponics is an innovative, smart, and sustainable agricultural technology that integrates aquaculture (farming of fish) with hydroponics in growing vegetable crops symbiotically. The correct implementation of aquaponics helps in providing healthy organic foods with low consumption of water and chemical fertilizers. Numerous research attempts have been directed toward real implementations of this technology feasibly and reliably at large commercial scales and adopting it as a new precision technology. For better management of such technology, there is an urgent need to use the Internet of things (IoT) and smart sensing systems for monitoring and controlling all operations involved in the aquaponic systems. Thence, the objective of this article is to comprehensively highlight research endeavors devoted to the utilization of automated, fully operated aquaponic systems, by discussing all related aquaponic parameters aligned with smart automation scenarios and IoT supported by some examples and research results. Furthermore, an attempt to find potential gaps in the literature and future contributions related to automated aquaponics was highlighted. In the scope of the reviewed research works in this article, it is expected that the aquaponics system supported with smart control units will become more profitable, intelligent, accurate, and effective. Full article

Artificial-intelligence methods and machine-learning models have demonstrated their ability to optimize, model, and automate critical water- and wastewater-treatment applications, natural-systems monitoring and management, and water-based agriculture such as hydroponics and aquaponics. In addition to providing computer-assisted aid to complex issues surrounding water chemistry and physical/biological processes, artificial intelligence and machine-learning (AI/ML) applications are anticipated to further optimize water-based applications and decrease capital expenses. This review offers a cross-section of peer reviewed, critical water-based applications that have been coupled with AI or ML, including chlorination, adsorption, membrane filtration, water-quality-index monitoring, water-quality-parameter modeling, river-level monitoring, and aquaponics/hydroponics automation/monitoring. Although success in control, optimization, and modeling has been achieved with the AI methods, ML models, and smart technologies (including the Internet of Things (IoT), sensors, and systems based on these technologies) that are reviewed herein, key challenges and limitations were common and pervasive throughout. Poor data management, low explainability, poor model reproducibility and standardization, as well as a lack of academic transparency are all important hurdles to overcome in order to successfully implement these intelligent applications. Recommendations to aid explainability, data management, reproducibility, and model causality are offered in order to overcome these hurdles and continue the successful implementation of these powerful tools. Full article

Conventional farming necessitates a large number of resources and infrastructure such as land, irrigation, manpower to manage farms, etc. Modern initiatives are required to automate conventional farms. Smart indoor farms offer the potential to remedy the shortfalls of conventional farms by providing a controlled, intelligent, and smart environment. This paper presents a three-dimensional perspective consisting of soilless farming, energy harvesting, and smart technologies, which could be considered as the three important characteristics of smart indoor farms. A six-layer smart indoor farms architecture has also been proposed, which explains how data are collected using various sensors and devices and then transmitted onto the cloud infrastructure for further analysis and control through various layers. Artificial lighting, smart nutrition management, and artificial climate control, to name a few, are some of the important requirements for smart indoor farms while considering control and service management factors. The major bottleneck in installing such systems is both the economical and the technical constraints. However, with the evolution of technology (and when they become widely available in the near future), a more favourable farming scenario may emerge. Furthermore, smart indoor farms could be viewed as a potential answer for meeting the demands of a sustainable agricultural revolution as we move closer to Agriculture 4.0. Finally, in order to adapt smart indoor farms and their study scope, our work has presented various research areas to potential researchers. Full article

The purpose of this study is to determine if aquaponic systems can reduce food insecurity in the semi-arid regions of Brazil and generate income for the beneficiaries. Aquaponics is a potentially sustainable way to produce food based on gardening, hydroponics and aquaculture. A case study, based on a project called Aquaponova, was developed. The aquaponic systems currently used in the project are non-commercial and designed for households with limited resources. The data based on six existing systems within this project were used to compare the costs and the benefits. The cost–benefit analysis covers four scenarios and three financing options. The results show that aquaponic systems have a large potential and can reduce food insecurity in semi-arid regions while generating income for the beneficiaries. Even if the system only produces 40% of the total estimated production, the system will still be feasible. However, the low opportunity cost of labour is an essential factor for obtaining these positive results. Moreover, the social benefits, such as a community spirit and the health benefits of the system, should not be underestimated. Full article