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New Generation Sustainable Technologies for Soilless Vegetable Production

Fernando Fuentes-Peñailillo
Karen Gutter
Ricardo Vega
2 and
Gilda Carrasco Silva
Instituto de Investigación Interdisciplinaria (I3), Vicerrectoría Académica (VRA), Universidad de Talca, Talca 3460000, Chile
Centro de Investigación y Transferencia en Riego y Agroclimatología (CITRA), Facultad de Ciencias Agrarias, Universidad de Talca, Talca 3460000, Chile
Departamento de Horticultura, Facultad de Ciencias Agrarias, Universidad de Talca, Talca 3460000, Chile
Authors to whom correspondence should be addressed.
Horticulturae 2024, 10(1), 49;
Submission received: 10 October 2023 / Revised: 18 November 2023 / Accepted: 27 November 2023 / Published: 4 January 2024
(This article belongs to the Section Vegetable Production Systems)


This review article conducts an in-depth analysis of the role of next-generation technologies in soilless vegetable production, highlighting their groundbreaking potential to revolutionize yield, efficiency, and sustainability. These technologies, such as AI-driven monitoring systems and precision farming methods, offer unparalleled accuracy in monitoring critical variables such as nutrient concentrations and pH levels. However, the paper also addresses the multifaceted challenges that hinder the widespread adoption of these technologies. The high initial investment costs pose a significant barrier, particularly for small- and medium-scale farmers, thereby risking the creation of a technological divide in the industry. Additionally, the technical complexity of these systems demands specialized expertise, potentially exacerbating knowledge gaps among farmers. Other considerations are scrutinized, including data privacy concerns and potential job displacement due to automation. Regulatory challenges, such as international trade regulations and policy frameworks, are discussed, as they may need revision to accommodate these new technologies. The paper concludes by emphasizing that while these sustainable technologies offer transformative benefits, their potential for broad adoption is constrained by a complex interplay of financial, technical, regulatory, and social factors.

1. Introduction

1.1. Background on Soilless Vegetable Production

Soilless vegetable production, encompassing hydroponic and substrate-based systems, has emerged as a viable alternative to traditional soil-based agriculture [1,2], particularly in regions with poor soil quality or limited arable land. Compared to soil-grown crops, this method can offer higher yields [3,4,5], efficient nutrient utilization, extension of harvest periods, and reduced susceptibility to soil-borne diseases [6,7,8]. In addition, the pressure of climate change on agricultural production is steadily increasing because of the reduction in freshwater availability [9,10], forcing producers to incorporate efficient production units such as hydroponic systems, which can aid in a context of limited water for agriculture [11]. However, a critical analysis reveals several challenges and considerations that underpin this growing field. First, if organic materials in soilless systems are not appropriately supplied to the crop, it can produce less robust microbial ecosystems, potentially affecting plant health and nutrient absorption [7]. Additionally, these systems often require a higher initial investment in infrastructure and technology, which can be a barrier for small-scale farmers or those in developing regions [12]. In addition, the environmental impact of soilless systems is still controversial; while they often use water more efficiently, the energy requirements for climate control and nutrient delivery can be significant [12,13]. Finally, there is a learning curve associated with managing these systems, requiring new skill sets that traditional farmers may not possess [14,15]. While soilless vegetable production presents exciting opportunities for addressing some of the challenges of modern agriculture, it also introduces a new set of complexities and considerations that must be critically examined.

1.2. Importance of Next-Generation Technologies in Advancing the Field

The importance of next-generation technologies in advancing the field of soilless vegetable production cannot be overstated. These technologies, ranging from AI-driven analytics to precision farming and advanced sensing systems, can revolutionize the industry by increasing yields, reducing resource consumption, and enhancing overall sustainability [16,17]. Nevertheless, as stated before, incorporating new types of technologies comes with several challenges to overcoming the constant increase in food demand because of population growth [18,19,20], potentially exacerbating existing inequalities within the sector. In addition, regulatory concerns, such as data privacy and environmental impact, remain unresolved [21,22,23], raising questions about their widespread adoption. Additionally, the rush to adopt new technologies may lead to inadequately tested implementations, risking failing to deliver promised benefits and introducing unforeseen risks; therefore, while next-generation technologies hold promise for soilless vegetable production, their adoption needs careful deliberation and balanced judgment to address these complex challenges.

1.3. Objective of the Review Article

This review article aims to comprehensively analyze the state-of-the-art technologies and methodologies in soilless vegetable production, focusing on next-generation innovations. This includes an in-depth examination of automation and precision farming techniques, sensing and tracking technologies, and the integration of artificial intelligence and data analysis applied in soilless horticultural crops, i.e., in aquatic systems and inert substrates (hydroponics), and cultivation with natural organic substrates [24]. The review also aims to identify these technologies’ advantages, limitations, and challenges. It offers a balanced perspective that considers their potential to improve yield and sustainability and the financial and technical considerations that may impact their widespread adoption. Furthermore, this article seeks to highlight gaps in current research and suggest directions for future studies, thereby serving as both a resource for practitioners in the field and a roadmap for researchers. In summary, the objective is to present a nuanced view that acknowledges the transformative potential of next-generation technologies in soilless vegetable production while critically examining the complexities and challenges of their implementation.

1.4. Methodology for Literature Selection and Analysis in Soilless Agricultural Technologies

In the pursuit of a comprehensive review of the current literature on soilless plant production, we meticulously established a set of selection criteria to ensure the inclusion of articles that were not only thematically relevant but also contributed significantly to the collective understanding of soilless agricultural practices, with a particular focus on technological innovation and sustainability. Our selection process prioritized articles that offered an all-encompassing view of the policies, technological advancements, and practical applications pertinent to soilless culture, especially hydroponics. We focused on studies that delved into soilless cultivation methods, emphasizing hydroponics and related innovative technologies. Research discussing integrating emerging technologies, such as sensors, artificial intelligence, and automation systems, was deemed essential to capture the evolving landscape of soilless farming. Sustainability and efficiency were at the forefront of our selection criteria. Articles that examined the sustainability aspects of soilless systems, including but not limited to resource management, energy consumption, and environmental impacts, were considered critical for inclusion.
Furthermore, we sought out case studies and real-world implementations of hydroponic systems that provided empirical data or firsthand accounts, thus ensuring that the review was grounded in practical, actionable insights. In recognition of the rapid pace of technological advancement in this field, we preferred recent publications, particularly those from the last few years, to ensure that our review reflected the most current state of the art. Our selection process yielded diverse studies that spanned a broad spectrum of research areas within soilless agriculture. This included articles that provided in-depth policy and economic analyses, explored innovative technologies for enhancing plant growth in soilless systems, and discussed resource management strategies aimed at reducing greenhouse gas emissions in greenhouse systems. Additionally, we included works that explored the concept of smart agriculture, such as the use of smart greenhouses and the assessment of solar energy, underscoring a growing trend toward precision agriculture and the adoption of renewable energy sources. We also addressed the challenges and prospects of adopting climate-smart agricultural practices, reflecting on the barriers to adoption and the potential for future opportunities. Case studies and practical experiences were also integral to our selection, providing valuable insights into the real-world implementation of soilless systems. Through this rigorous selection methodology, our review encapsulates a wide array of topics within soilless agriculture, offering a nuanced and well-rounded understanding of the field that spans from high-level policy analysis to detailed technological applications.

2. Hydroponic Systems

2.1. Overview of Hydroponic Systems

Hydroponic systems are a method of growing plants without soil, extracting the necessary nutrients for their growth from a nutrient solution [25]. These systems have emerged as a cornerstone technology in soilless vegetable production, offering a range of benefits from resource efficiency [20,26] to optimized plant growth [27]. One of the critical advantages of hydroponic systems is the ability to precisely control nutrient levels, electrical conductivity (EC), pH, and oxygenation [28], thereby optimizing plant growth and resources. Hydroponic systems are more water-efficient than traditional soil-based cultivation, making them an attractive option for sustainable agriculture. These systems can be categorized into several types, including Nutrient Film Technique (NFT), Deep Water Culture (DWC), and Aeroponics, each with unique advantages and applications. NFT, for instance, employs a thin film of nutrient solution that flows over the roots, powered by a water pump, providing a constant supply of nutrients and oxygen [29,30]. This system is particularly effective for leafy greens and herbs, which require less support and can thrive in these conditions.
On the other hand, DWC submerges the plant roots in a nutrient solution, using air stones to oxygenate the water [31]. This system is well-suited for plants that require more support and a stable environment, such as tomatoes and cucumbers. Aeroponics represents the most technologically advanced form of hydroponics, where nutrient solution is misted directly onto the roots, maximizing oxygen exposure [27]. This system has shown promise in rapid plant growth and resource efficiency but requires more precise control and monitoring [27,32]. The main characteristics, advantages, and disadvantages of these systems are detailed in [30,33].

2.2. Recent Technological Advancements in Hydroponics

Recent technological advancements in hydroponics have significantly transformed the field, making it more efficient, sustainable, and user-friendly. For instance, integrating Internet of Things (IoT) devices allows real-time monitoring and control of key environmental variables, such as temperature and nutrient levels, facilitating data-driven decision-making [34,35]. In addition, introducing specialized LED grow lights has optimized plant growth through targeted light spectrums and reduced energy consumption [36]. Artificial Intelligence and machine learning algorithms are now being employed to analyze data and predict plant growth patterns and potential diseases, enabling preventive and more effective interventions [37]. Additionally, robotics has been introduced to automate tasks from planting to harvesting, reducing labor costs and human error [38]. Advanced water recycling systems have further increased the water efficiency of hydroponic setups [39,40], and new software platforms enable remote monitoring and control [41,42], ideal for large-scale operations and multi-site growers, setting the stage for a more efficient and sustainable future in soilless vegetable production. The main advanced technologies currently applied in hydroponics, along with their advantages and disadvantages, are listed in Table 1.
As seen in Table 1, advantages and disadvantages are observed when incorporating technologies in the productive soilless system, which must be taken into account before considering implementing hydroponic systems. In terms of the advantages, hydroponic systems can be equipped with automation and sensor technology, allowing for remote monitoring and control [52]. This enables growers to make real-time adjustments to environmental conditions, nutrient delivery, and other parameters [53]. In this sense, the main advantages of the incorporation of advanced technologies in hydroponics are related to higher efficiency and optimization in the use of resources, being able to precisely control the delivery of water, nutrients, and light to plants, minimizing waste, and optimizing resource utilization [45,54,55]. In addition to this, hydroponics typically uses significantly less water compared to traditional soil-based agriculture [56], which can be especially advantageous in regions with water scarcity or drought conditions. Additionally, the controlled environment in hydroponics allows for accelerated plant growth [28], which means quicker crop turnover and increased productivity, providing a stable and controlled environment for plants, resulting in consistent product quality and reducing the risk of soil-borne diseases and pests. Finally, it is also important to consider that the local climate, market demand, and regulatory factors play a role in determining the extent to which advanced hydroponic technologies are advantageous for a particular agricultural operation. In this sense, when these technologies are well-planned and managed, they can provide significant benefits in terms of resource efficiency, product quality, and increased yield.
Regarding the disadvantages of these technologies, several of them are mentioned in Table 1; however, the most common disadvantage is related to the high initial investment costs, which in hydroponic systems are mainly related to costs that include items such as grow lights, pumps, nutrient delivery systems, climate control systems, and specialized growing media [57], which are items that allow the construction of an advanced technological production system. These costs can vary significantly depending on the scale and complexity of the hydroponic setup. For instance, for large-scale hydroponic operations, the construction or retrofitting of greenhouses or indoor growing spaces can be a substantial expense, influenced by factors such as location, size, and design; however, studies such as [58] have tested the incorporation of technologies such as the use of hybrid renewable energy systems (HRES) to reduce operating cost and emissions, concluding that the final cost of cultivating lettuce under the established experimental conditions was similar to the cost of traditionally imported lettuce, which would indicate a high potential in terms of the competitiveness of hydroponically grown produce. Another disadvantage of incorporating new technologies in hydroponic systems is that they require technical knowledge and expertise to operate them [47,59]. This includes cleaning, nutrient replenishment, equipment checks, and maintenance costs. Finally, hydroponic systems often rely on artificial lighting and climate control, which can lead to high energy bills. These disadvantages highlight the need to incorporate cost-effective solutions that aid in lowering the final production costs, generating a more competitive final product, and fulfilling future needs for increased food production.

2.3. Impact of Hydroponic Systems on Soilless Vegetable Production

The impact of hydroponic systems on soilless vegetable production is transformative, offering a host of benefits but presenting specific challenges that merit critical scrutiny. On the positive side, hydroponic systems are highly resource-efficient, using less water than traditional soil-based farming, which makes them ideal for regions facing water scarcity [60]. They also allow for a controlled environment where variables like nutrient levels and pH can be precisely managed, leading to optimized plant growth and potentially higher yields [61]. Additionally, these systems are particularly beneficial in urban settings where space is limited, thanks to their efficient use of space [62], especially when combined with vertical farming techniques [63]. Additionally, the reduced need for pesticides in these controlled environments contributes to healthier produce [45].

2.4. Scalability and Replicability of Soilless Cultures

In the current context of global warming, along with the increase in urbanization and the reduction in arable land, soilless culture systems need to be expanded or scaled up to meet the increasing demand or production needs and to maintain consistent plant growth and yield while increasing the number of plants or the size of the growing area [64]. Several cases have reported successfully incorporating technological advances to optimize plant production under soilless culture systems. However, a critical analysis reveals that these success stories may only sometimes be universally applicable and could sometimes paint an overly optimistic picture of the technology, given that some of these studies may have been conducted under controlled conditions or with significant financial backing, making it unclear how scalable or replicable these successes are for average farmers. In addition to this, focusing on success stories may overshadow the challenges and failures, which are equally important for understanding the limitations of hydroponic systems; therefore, while case studies and success stories offer invaluable insights into the capabilities of hydroponic systems, a more nuanced and critical approach is needed to understand their broader impact, limitations, and the conditions under which they can be successfully implemented. Nevertheless, recent studies where different factors involved in soilless production are assessed become relevant to generate a big picture regarding the future of this field. In this sense, considering that soilless production can be limited by factors such as the availability of resources such as water, nutrients, and energy, as well as the cost and complexity of the system [64], several studies have reported the incorporation and analysis of technological advances in hydroponic systems over different crops and over different conditions, to overcome the limitations. Such is the case of [65], who carried out a study to assess the effects of different nutrient solutions on the growth and weight of two lettuce cultivars grown in a floating hydroponic system, indicating that hydroponic production can be a cost-effective and easy option for organic vegetable production if the nutrient solution is managed according to the needs of the crop. In another case, ref. [66] compared an aquaponic system, incorporating three different hydroponic systems to assess their effect on tomato yield and morphological and biochemical impact quality, indicating that the choice of the system had little effect over those properties; however, the system that incorporated drip irrigation presented slightly better results in terms of higher oxygen radical absorbance capacity, indicating the possibility of producing fruit with a higher health value. A study by [42] analyzed the response of kale to liquid inorganic nutrients and different planting media in a DWC hydroponic system, indicating a high effect on chlorophyll content and yield when using specific combinations of planting media and liquid inorganic nutrients. In another study on kale, ref. [67] analyzed the effect of different nutrient solution depths on the growth and phytochemicals accumulation in this hydroponic-grown vegetable to assess the issue of low dissolved oxygen in a nutrient solution, indicating that there was a positive effect on kale growth when nutrient solution depth increased, where a 2-cm deep nutrient solution could promote kale growth, and 3-cm could have the potential of improving kale quality.

3. Substrate-Based Systems

3.1. Introduction to Substrate-Based Systems

In the context of soilless vegetable production, substrate-based systems offer an alternative to hydroponic methods, providing a unique set of advantages and challenges critical to understanding the advancement of the field. Unlike hydroponics, which relies solely on nutrient-rich water solutions, substrate-based systems use inert media such as coconut coir, perlite, or rock wool to support plant roots [64]. These substrates serve as a reservoir for nutrients and water, allowing for less intensive management practices than hydroponics. This section introduces the fundamental aspects of substrate-based systems, including the types of substrates commonly used, their benefits, and the innovations shaping this method of soilless cultivation. As next-generation technologies evolve, substrate-based systems are becoming increasingly sophisticated, incorporating automation, real-time monitoring, and data analytics to optimize crop yield and resource utilization. Therefore, understanding the intricacies of substrate-based systems is essential for researchers, practitioners, and policymakers who are invested in the future of sustainable and efficient soilless vegetable production.

3.2. Substrate Materials and Their Benefits

In the rapidly evolving field of soilless vegetable production, the correct selection of substrate materials is a critical development that offers promising benefits but also presents challenges that require thoughtful analysis. On the positive side, newer substrates, such as biochar, are sustainable and provide superior nutrient retention capabilities, allowing for more efficient fertilizer use and reducing the environmental impact of nutrient leaching [68,69,70,71]. The advent of composite substrates, which combine different materials to optimize physical and chemical properties, adds another layer of versatility, offering tailored solutions for specific crop needs. However, these benefits often come at a cost—both literally and figuratively. Advanced substrates are generally more expensive than traditional options [45], making them less accessible for small-scale farmers. In addition, the availability of these new materials can be limited, especially in certain regions, which hampers their widespread adoption. Using new substrate materials also necessitates a learning curve, requiring adjustments in cultivation practices and additional expertise for optimal results. Moreover, while many of these substrates are promoted as sustainable options, the environmental impact of their production processes, including potential energy-intensive manufacturing and transportation emissions, should be considered [72]. An overview of some of the substrate materials in soilless agriculture is shown in Table 2.
Considering those above, it is important to highlight that, in the field of soilless vegetable production, substrate-based systems have evolved to include various materials and technologies, each with its own set of advantages and drawbacks that warrant a comprehensive comparative analysis. As was discussed, newer substrates such as coconut coir and biochar offer superior water and nutrient retention capabilities and environmental benefits such as biodegradability; however, these materials can be more expensive and require specialized management techniques [45]. Technological advancements have also led to the development of systems combining multiple substrate benefits, offering tailored solutions for specific crop needs [64]. Furthermore, the choice of substrate can have implications for the system’s environmental impact in terms of resource use and potential for waste generation. In this sense, while there is no one-size-fits-all solution, the choice of substrate and associated technologies in substrate-based systems should be guided by various factors, including cost, management complexity, crop-specific needs, and environmental impact.

3.3. Innovations in Substrate-Based Cultivation Techniques

In soilless vegetable production, innovations in substrate-based cultivation techniques fundamentally alter the landscape, offering many advantages but presenting several challenges that require in-depth analysis. Automated nutrient delivery systems, for instance, are a game-changer, given that these systems are designed to directly provide precise amounts of nutrients to the substrate, thereby eliminating the guesswork and manual labor traditionally associated with fertilization [80]. The result is improved plant health and the potential for significantly increased yields, making this innovation particularly beneficial for commercial-scale operations [81]. Real-time monitoring technologies, often facilitated by advanced sensors embedded directly into the substrate, are another groundbreaking development. These sensors can measure various parameters, such as moisture levels, nutrient concentrations, and substrate pH, sending these data to a centralized system [41], meaning that growers can make immediate, data-driven adjustments to their cultivation practices, optimizing resource utilization. This is a monumental step forward in terms of both sustainability and operational efficiency. However, the energy requirements for running these automated and monitoring systems should be noted. While they may optimize water and nutrient use, they also require a continuous power supply, which could increase the operation’s carbon footprint if the energy is sourced from non-renewable resources [82,83].

4. Automation and Precision Farming

4.1. Role of Automation in Soilless Vegetable Production

Automation is increasingly becoming a focal point in soilless vegetable production, offering transformative benefits but posing challenges that require critical evaluation. Automation technologies, such as automated nutrient delivery systems [80], climate control [16], and robotic harvesting [84], can revolutionize the industry by increasing efficiency, reducing labor costs, and enhancing crop productivity. These systems allow for precise control over various environmental factors, from nutrient levels to humidity and temperature, enabling optimized growing conditions that lead to higher-quality produce. The data-driven nature of these technologies also provides valuable insights into crop performance, allowing for timely interventions and more effective resource management. The application of automation technologies into soilless vegetable production has been observed in studies such as [85], which aimed to identify the optimal irrigation level in a microgreen production based on the use of a dielectric moisture sensor to generate a low-cost automated irrigation system, contributing to the automation of precision irrigation in hydroponically grown microgreens. In a similar study carried out by [86], a system for wireless irrigation management was developed and tested on soilless basil, concluding that employing a wireless sensor network to monitor substrate water conditions in real-time, along with detailed insights into how varying water availability impacts plants, proves to be a valuable resource for optimizing precision irrigation in soilless basil cultivation in greenhouses. A study conducted by [87] on an automated system for fertigation control in soilless tomatoes growing in a sand substrate under greenhouse conditions concluded that the developed control system could effectively adjust fertigation cycle frequency according to plant transpiration, reducing unnecessary applications under varying climatic conditions.
Based on the above, it should be noted that while automation holds great promise for improving efficiency and productivity in soilless vegetable production, it also introduces financial and technical challenges that need to be carefully considered for responsible and long-lasting adoption.

4.2. Application of Precision Farming/Agriculture Techniques in Hydroponics

In soilless vegetable production, applying precision farming or precision agriculture techniques to hydroponics and substrate-based systems is an emerging trend with far-reaching implications, given that precision agriculture is a tool able to generate an increase in production and quality, reducing the use of resources and its environmental impact [88]. These techniques, which leverage advanced sensors, data analytics, and automation, promise to optimize every aspect of the growing process, from nutrient delivery to environmental control. In hydroponic systems, for example, precision farming can lead to more efficient nutrient utilization, reducing waste and potentially lowering the environmental impact. Similarly, real-time monitoring can help fine-tune irrigation schedules and nutrient levels in substrate-based systems, thereby conserving resources and improving yield [89]. Several researchers have reported the use of precision farming applied to hydroponics. For instance, a study carried out by [47] developed a monitoring and controlling system for precision farming based on the IoT concept and fuzzy logic for the monitoring of water and nutrient needs of lettuce and bok choi, indicating that this monitoring system allowed assessing the needs of the crop in real-time, which translated into larger leaves for both crops. In another case, ref. [90] proposed an automatic management system on a tropical hydroponic system, focusing on reducing information exchange between sensors, obtaining a simpler system in data obtention and analysis, and aiding in managing hydroponic crops. In a study carried out by [91] on a greenhouse with hydroponic crops, a low-cost sensor based on IoT to develop control processes for precision agriculture was developed, resulting in a series of benefits related to cost, energy saving, smart development, and most importantly, an increase in acceptance by producers. Studying hydroponic saffron cultivation, ref. [46] proposes a novel approach to sensor selection, aiming to optimize crop cultivation in an artificial environment using technology, specifically IoT and sensors, creating an automated and controlled cultivation system.
Given the above, it can be noted that implementing automation and precision farming techniques in soilless vegetable production presents a complex interplay of benefits and challenges that warrant a nuanced analysis. On the benefit side, automation and precision farming offer unparalleled advantages in terms of operational efficiency, resource optimization, and yield improvement. Automated systems can handle exact tasks like nutrient delivery and climate control, reducing human error and freeing up labor for other jobs. With its data-driven approach, precision farming allows for real-time adjustments to various growth parameters, leading to more efficient use of resources, such as water and fertilizers. This can increase yield and make the production system more sustainable by minimizing waste and environmental impact. However, these advantages come with challenges that must be addressed. As mentioned before, the initial cost of implementing these advanced technologies and energy consumption can be significant, posing a financial barrier for small-scale farmers. This raises concerns about the extent of these benefits across the agricultural sector. These concerns need to be addressed properly and in a timely manner to fulfill the current need for an increase in food production in the context of a reduction in the cultivable surface and under the scenario of severe climate change.

4.3. Prospects and Trends in Automated Soilless Crop Systems

In the field of soilless vegetable production, the prospects and trends in automated systems are both promising and fraught with complexities that demand critical scrutiny. On the optimistic side, advancements in the era of Agriculture 4.0 have been accompanied by technologies such as IoT artificial intelligence, data analytics, and big data, among others, which can generate tools that, in the end, can address issues such as food safety, data analysis, and improved crop management [92]. These technologies will likely make soilless systems more efficient, scalable, and even self-adaptive, adjusting to environmental variables in real time for optimized crop yields. Integrating IoT devices could further streamline operations, allowing for remote monitoring and control, which is particularly beneficial for large-scale commercial setups. Sustainability is another area where automation could make significant strides, especially with the development of energy-efficient systems and closed-loop nutrient recycling processes. These technological advancements have transformed traditional farming methods into automated systems, introducing a new era of agricultural innovation driven by the IoT and fundamentally altering how farming is conducted today [93]. In this sense, while the future of automated soilless crop systems holds immense promise for transforming the industry in terms of efficiency, scalability, and sustainability, the adoption of these systems should be done in a way that is responsible, considering the social and environmental impacts of the system [15].

5. Sensing and Monitoring Technologies

5.1. Importance of Sensing and Monitoring in Soilless Vegetable Production

In the evolving landscape of soilless vegetable production, the importance of sensing and monitoring technologies cannot be overstated, yet they come with complexities that merit a critical examination. Sensing and monitoring technologies, such as soil moisture sensors, nutrient level detectors, and environmental sensors, are the backbone of data-driven agriculture [94,95,96]. They provide real-time insights into various growth parameters, enabling timely interventions that significantly improve crop yields and resource efficiency. For instance, soil moisture sensors can help optimize irrigation schedules, reducing water consumption. In this sense, ref. [93] developed a cost-effective control system for the optimization of soilless crop irrigation through a prototype of an intelligent gravimetric tray that records the irrigation and drainage volumes and drainage pH and electrical conductivity (EC), as well as the temperature, EC, and humidity of the substrate. Regarding nutrient content, ref. [97] determined the nutrient content of hydroponically cultivated microgreens with a novel immersible silicon photonic sensor to determine the optimal harvest time. These technologies are particularly crucial in soilless systems, where the margin for error is often smaller than in traditional soil-based agriculture. However, implementing these advanced sensing and monitoring systems is not without challenges. The initial cost of these technologies can be a significant barrier for small-scale farmers, and the data collected by these sensors often require sophisticated analysis, necessitating a level of resources and expertise that some farmers may not possess [98,99]. Ref. [99] found that one of the barriers to the adoption of agricultural innovations by farmers is the level of learning investment, initial investment cost, and additional labor required when adopting a farm innovation, but also found that smallholder farmers continuously adapt to changing circumstances that affect their farming businesses. Age, gender, education level, years of farming, and involvement in off-farm activities are influencing forces on the adoption of new technologies. These factors could limit or boost the adoption of these beneficial technologies.

5.2. Advances in Sensor Technologies for Nutrient Management and Environmental Control

In the domain of soilless vegetable production, advances in sensor technologies for nutrient management and environmental control are increasingly becoming pivotal, yet they introduce a set of intricate challenges that necessitate a thorough critical analysis. Cutting-edge sensors now offer unprecedented accuracy in measuring nutrient concentrations, pH levels, and environmental factors such as temperature, moisture, and humidity [100]. This level of precision is a game-changer for soilless systems, where optimal nutrient and environmental conditions are crucial for maximizing yield and quality. For instance, nutrient sensors can automatically adjust the composition of nutrient solutions in real-time, ensuring that plants receive exactly what they need for optimal growth. Ref. [101] developed an automatic system capable of performing water delivery automatically when the water level is less than a minimum predefined level and adding the nutrients automatically when the nutrient solution concentration is below 800 ppm, using a GP2Y0A21 proximity sensor as a water level detector and a TDS sensor as a detector of electrical conductivity of the nutrient solution. In this system, a servo motor turns on a valve in the nutrient container. To improve macronutrient detection for more efficient management of nutrients in crops growing in a closed hydroponic system, ref. [102] developed a system based on small, portable ion-selective electrode (ISE) sensors, which can directly measure the analyte with a wide range of sensitivity. The great benefit of this system is that it would allow the automatic sensing of nutrients in greenhouse hydroponics.
Environmental sensors can similarly adjust climate control systems to maintain ideal growing conditions. These advancements significantly reduce the margin for human error and can lead to more sustainable practices by minimizing waste of resources such as water and fertilizers. For example, a researcher from Indonesia [103] built a simple micro-climate control system to control the temperature and humidity in a hydroponic lettuce greenhouse by implementing a fuzzy logic controller based on pre-defined thresholds. Similarly, ref. [104] presented an effective method that monitors and controls environmental parameters using sensors and actuators for tunnel farming and hydroponics, resulting in increased crop yield and water savings. A more comprehensive approach was taken by Chinese researchers [105], who developed an environment monitoring system for hydroponics and aquaculture applications. The system employed multiple sensors, including dissolved oxygen and water temperature, outdoor meteorological parameters, soil temperature and humidity, indoor temperature, humidity and carbon dioxide concentrations, and humidity of hydroponic plant leaves. The data from these sensors are then transmitted to the cloud, processed, and displayed to the user through a web platform and a mobile application to support hydroponic and aquaculture production management. Another benefit of using sensors was proposed by [106] to estimate the condition of hydroponic tomato plants in real-time; they developed an environmental control system with wireless sensors that supply the appropriate amount of hydroponic liquid for the tomatoes based on evapotranspiration, with errors of less than 3%.

5.3. Real-Time Monitoring Systems for Optimizing Crop Growth and Resource Utilization

In the specialized field of soilless vegetable production, the advent of real-time monitoring systems for optimizing crop growth and resource utilization marks a significant leap forward, but it also brings forth a set of multifaceted challenges that require critical evaluation, considering, for example, that hydroponics necessitates more monitoring and micromanagement than traditional plant cultivation. Real-time monitoring systems, which often integrate various types of sensors and data analytics platforms, provide immediate feedback on a range of critical parameters such as plant development status, nutrient levels, moisture content, and environmental conditions [107]. This immediacy allows for dynamic adjustments that significantly improve yield and resource efficiency. With the aim to improve and automate the irrigation process of lettuce grown in a hydroponic system, ref. [108] proposed a real-time monitoring system that considers the use of IoT technology for sensing important factors of the nutrient solution, such as pH, total dissolved solids (TDS), temperature, and humidity. An application was developed that notifies the user of the abovementioned parameters and initiates irrigation automatically through a click on the app. A different approach was taken by [109], where the application of computer vision in a smart system for real-time monitoring, quality control, and condition assessment of romaine lettuce grown in aquaponic was researched. The proposed system uses image-processing techniques, image segmentation, deep learning, and regression analysis to estimate the size of the crops (growth rate) and then correlate it with their fresh weight, with the aim of using it in real scenarios to promote autonomous farms.
In nutrient film technique (NFT), two key parameters that affect plant growth are pH and EC. To maintain these parameters within a set range, ref. [110] implemented a real-time fuzzy logic control for an NFT-based hydroponic system using an IoT environment composed of a wireless sensor network, data logger, and machine-to-machine actuators (pumps). With this system, they were able to stabilize the pH and EC values in less than 15 min.
Table 3 summarizes different real-time monitoring systems, their advantages, and disadvantages in the smart farming agriculture field. The yield and success of a crop depend on the parameters measured by these sensors for the continuous monitoring of environmental conditions and the automation of various aspects of farming operations [111]. The advantage of this approach is that sensors are lightweight, suitable to be deployed in harsh conditions, and have improved accuracy, removing or reducing the risk of human error inherent to manual data capturing and allowing real-time monitoring and remote access when integrated into network gateways. These systems can contribute to increased plant growth [112,113,114], reduce water consumption [115,116], improve environmental and agricultural parameters [117,118,119,120,121], reduce manpower [122], allow remote monitoring [123,124,125,126], and function as an early warning system [127]. Additionally, IoT devices can allow increased automation of operations. However, real-time monitoring requires wireless connectivity because IoT devices are the building blocks of wireless sensor networks (WSN), which is not always available in farms or rural areas. Another significant limitation to the adoption of these systems is cost, which often prevents its adoption by small farmers, who have increasingly smaller profit margins, and also by larger farms, as the scalability of systems is not fully explored. The high cost is related to the degree of customization required for installation, research and development, updates and maintenance, and costs associated with data transmission. There are other technical considerations, such as the power source. In many cases, there is no access to constant power; therefore, systems depend on batteries and solar panels for their operation, so consumption and energy conservation becomes an important limitation. Multiple sensors produce huge amounts of data that require storage (locally or cloud-based) and computing power to be analyzed. This, associated with the lesser technical expertise of farmers, becomes a major setback to IoT-based monitoring [128,129].

5.4. Case Studies Demonstrating the Effectiveness of Sensing and Monitoring Technologies

In soilless vegetable production, real-time monitoring systems and sensor technologies have shown promise in optimizing yield and resource utilization, as evidenced by various case studies. Countless systems that rely on different monitoring sensors, such as for water level, pH, air humidity, air temperature, light intensity, CO2 concentration, soil moisture and temperature, and EC, among others, to then apply a smart decision workflow to control actuators, pumps, fans, lights, and other equipment with different levels of automation have been proposed and developed by several authors [143,144,145,146,147,148,149,150,151,152,153,154,155]. However, while case studies often highlight significant improvements in yield and efficiency, they are usually conducted in controlled or well-funded environments, casting doubt on their generalizability to smaller operations. Additionally, these studies often focus solely on positive outcomes, neglecting to address challenges such as technical difficulties, ongoing operational costs, and data security risks. In this regard, the adoption of smart sensing and monitoring technologies is accompanied by security issues that can compromise farming operations, data integrity, and privacy. Small vulnerabilities can become cybersecurity threats, considering that smart farms are very interconnected systems and farmers have limited awareness of the risks associated with smart agriculture systems [156]; if someone exploits those vulnerabilities, it can create the potential to disrupt the economies of countries. These attacks can be at the data level (leakage of confidential data, insertion of false data, misconfiguration), at the network and equipment level (radio frequency jamming or signal disruption, malware injection, denial of service (DoS) and distributed denial of service (DdoS), Botnet or central malicious system, data transit attacks, autonomous system hijacking, node capture), at the chain level of supplies (third party attacks, software update attacks, data fabrication), or other types of attacks (unauthorized access, cloud computing attacks) [157,158]. Another aspect is the energy requirements for these systems; if sourced from non-renewable resources, this could negate some of the sustainability benefits. Therefore, while real-time monitoring and sensing technologies offer groundbreaking opportunities for soilless vegetable production, their cost, technical expertise, data security, and sustainability limitations must be critically examined when implementing a new farm project for a more comprehensive understanding of their long-term viability and adoption.

6. Artificial Intelligence and Data Analytics

6.1. Clarifying AI Applications in Soilless Agriculture: Beyond Predictive Analytics

The advent of Artificial Intelligence (AI) and Data Analytics has revolutionized many industries, with agriculture being no exception. Integrating these technologies into soilless crop systems has opened a new frontier for innovation and efficiency. This section delves into the multifaceted role of AI in these systems, distinguishing between predictive algorithms and machine learning techniques and exploring their unique contributions to soilless agriculture. Predictive algorithms have long been employed to forecast various aspects of crop management, from nutrient requirements to yield estimation. These algorithms, which can be as straightforward as linear regression models or as complex as non-linear time-series analyses, serve as the backbone for decision-making processes. They are particularly valuable in soilless crop systems where precision and control are paramount. Machine learning, a dynamic subset of AI, extends beyond predictive capabilities to include classification, clustering, and reinforcement learning. In the controlled environments of soilless agriculture, machine learning algorithms leverage large datasets to uncover patterns and insights imperceptible to the human eye or traditional methods. For instance, neural networks may be utilized to predict plant stress levels, while support vector machines could classify the health of plants based on spectral data. However, it is essential to recognize that not all predictive algorithms in soilless crop systems employ machine-learning techniques. Various statistical methods and mathematical models rely on predefined rules and equations, offering a different approach to prediction. These non-ML predictive algorithms are crucial in scenarios where transparency and interpretability are necessary or data may be too scarce or noisy for complex machine-learning models. Furthermore, the application of computer vision techniques extends into the realms of robotics and cybernetics, which are increasingly becoming intertwined with AI in modern agriculture. In soilless farming, computer vision is not only used for static image analysis but is also integrated into robotic systems for dynamic tasks such as automated harvesting or real-time growth monitoring. As we continue exploring AI’s capabilities and applications in soilless crop systems, it is imperative to establish a clear understanding of the various technologies at play. This section aims to clarify by dissecting the roles and responsibilities of different AI applications and setting the stage for future innovations that will further enhance the precision and productivity of soilless agriculture.

6.2. Integration of Artificial Intelligence (AI) in Soilless Crop Systems

Artificial Intelligence (AI), referred to as the simulation of human intelligence in machines that are programmed to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation, could revolutionize soilless crop systems by automating complex decision-making processes [159], optimizing resource allocation, and even predicting future growth patterns. AI algorithms can analyze vast amounts of sensor data to make real-time adjustments to nutrient levels, irrigation schedules, and environmental controls, maximizing yield and minimizing resource loss [160,161]. Table 4 summarizes the main advantages and disadvantages of AI applications over soilless systems.
According to a great number of articles and applications of artificial intelligence in agriculture in general and in soilless systems in particular, the main benefits of this technology include increased efficiency, improved crop quality, reduced labor costs, better decision-making, and more sustainable practices [189]. The optimization of crop yields by efficiently managing factors such as water, fertilizer, and pest control is one of the main advantages of AI in soilless agriculture. This optimization leads to higher returns on investment for farmers. AI enables more precise control over irrigation and soil fertilizer application, resulting in higher resource use efficiency. For example, ref. [164] evaluated the performances of four bio-inspired algorithm-optimized extreme learning machine (ELM) models for predicting daily reference evapotranspiration (ETo) across China and demonstrated that the values predicted by all ELM models agreed well with the FAO-56 Penman–Monteith values. This means that farmers can achieve higher yields with the same resources or maintain yields by lowering resource use, contributing to sustainable agricultural practices. Another benefit of AI is the reduction in pests and diseases by enabling early detection and diagnosis through image signal processing techniques [190]. This early diagnosis, such as identifying powdery mildew, allows for timely intervention and reduces the impact of pests and diseases on crops. Energy use is a key factor in modern agriculture, and AI can contribute to efficient management between smart greenhouses and soilless systems, allowing cost savings and improving sustainability. This involves the integration of various technologies, such as bio-inspired algorithms, automation, and dynamic pricing based on real-time metrics. Regarding automation, robotic systems driven by AI are being utilized for various tasks, including irrigation, harvesting, counting, and other mechanical farming processes. This automation not only improves efficiency but also reduces the labor intensity of farming operations. Additionally, AI-powered robot platforms can perform unsafe tasks such as toxic pesticide application and UV-C treatments to impede the spread of powdery mildew [191,192,193]. Taking into consideration the above, AI technologies, including bio-inspired algorithms and machine learning, enable precision agriculture. This precision ensures the accurate and targeted application of resources, minimizing waste and maximizing productivity. This leads to enhanced agricultural sustainability. By addressing challenges such as resource optimization, pest control, and energy efficiency, AI contributes to environmentally friendly and economically viable farming practices. AI is no longer just a research topic, as commercially viable AI technologies for agriculture are starting to appear [194]. This suggests ongoing advancements in the field, providing farmers with access to more sophisticated and effective tools for improving their farming practices. However, this promising frontier also presents several challenges that require critical consideration. One of the main constraints of AI implementation is cost, one reason being the expensive sensors (up to USD 1000+) [195], considering that in many instances, multiple sensors, cameras (RGB, thermal, multispectral), and other hardware are required, and for automation, actuators, fans, heathers, lightning, dehumidifiers, and motors are required. As was stated by [196] the cost of installation of AI systems on farms was enormous and out of reach for smallholder farmers. In contrast, larger farms can take the risk of absorbing this higher cost and are more willing to invest [197]. To implement these technologies, technical expertise is needed, as it can be complex and require specialized knowledge to implement and maintain, requiring greater computational resources. This can be a challenge for growers who are not familiar with the technology or who lack technical expertise. That is the situation in Europe, where the aging population and shortage of young progressive farmers raise concerns about technology adoption [198], considering that it is well-known that less educated and older farmers are less inclined to adopt precision farming technologies [197]. There are also obstacles to transferring this technology to developing countries due to farm size, less connectivity, reduced infrastructure, and economic issues, among others. Concerning predictive models, ref. [199] indicated that the absence of appropriate historical data poses a significant obstacle to the integration of AI systems in agriculture, which is necessary for accurate predictions and the training of AI and ML systems. Moreover, studies have suggested that the creation of algorithms for assessing greenhouse parameters should be calibrated with local conditions and specific requirements [200].

6.3. AI-Based Decision Support Systems (DSS) for Optimizing Cultivation Parameters

Using AI-based decision support systems (DSS) in soilless crop systems offers a transformative approach to optimizing cultivation parameters such as nutrient levels, irrigation timing, and environmental conditions. These systems can analyze large datasets, identify patterns, and make predictive recommendations, enhancing yield and resource efficiency [201,202,203]. AI has played a pivotal role in transforming agriculture and protecting it from various threats, such as weather, population growth, labor issues, and food security concerns. The numerous applications of AI in agriculture, including tasks like irrigation, weeding, and spraying, are often implemented through sensors in robots and drones. These technologies help conserve water, reduce pesticide and herbicide use, preserve soil fertility, and optimize labor, leading to increased agricultural output and improved quality [204]. In this regard, ref. [205] presented the development and implementation of a smart agriculture IoT system based on deep reinforcement learning (DRL). The system integrates advanced information technologies, including AI and cloud computing, with agricultural production to increase food production while reducing the consumption of resources. The AI model technique used in the cloud layer is DRL, which makes immediate, intelligent decisions, such as determining the water needed for irrigation to enhance crop growth.
In an innovative way, the research by [206] discusses the results of an international competition focused on autonomous greenhouses that combined horticultural expertise with AI to improve fresh food production with fewer resources. Teams applied various AI algorithms, ranging from supervised and unsupervised learning to reinforcement machine learning, to control the greenhouse environment remotely. The use of AI demonstrated the potential to outperform traditional manual growing methods. To further optimize the operation of AI technologies for agriculture, ref. [207] proposed a novel approach using AI-based soft sensors combined with remote sensing models utilizing deep learning architectures. The study involves preprocessing techniques to clean missing data and remove noise from agricultural land images, with subsequent feature representation and classification processes demonstrating significant improvements in computational efficiency and accuracy in agricultural applications. Building upon these foundations and discussing the diversity of AI-based DSS and their specific applications in soilless crop systems is crucial. AI-based DSS vary significantly in complexity and function, from simple regression models to sophisticated deep learning algorithms capable of strategic planning and real-time adaptive management. For example, while some DSS may employ traditional statistical methods for short-term forecasting, others leverage complex neural networks for comprehensive analysis and long-term planning. These systems are tailored to the unique needs of soilless operations, considering factors like production scale, crop types, and automation levels. Moreover, integrating AI-based DSS in soilless agriculture goes beyond mere data analysis; it encompasses the entire decision-making process, from data collection and preprocessing to action implementation. This holistic approach ensures that every soilless farming operation is optimized for efficiency and sustainability.
In conclusion, AI-based DSS are indispensable in soilless crop systems, providing a level of analysis and foresight that is unparalleled. These systems not only contribute to more sustainable and productive farming practices but also represent a significant advancement in the field of precision agriculture. Our discussion aims to clearly understand the diverse and powerful AI techniques that drive decision-making in soilless agriculture, addressing the specific needs and challenges of this innovative farming approach [208].

6.4. Potential Challenges and Ethical Considerations in AI-Driven Cultivation

However, some related aspects must be considered when analyzing this technological advancement. First, the effectiveness of AI-based decision support systems depends on the quality and quantity of data they are trained on. The potential for algorithmic bias also exists, especially if the data used to train these systems do not represent diverse growing conditions and crop types. Poorly trained systems can lead to incorrect recommendations, potentially harming crop yield and wasting resources [209,210,211]. Second, the technical complexity of AI systems often requires specialized expertise to set up, operate, and interpret, creating a knowledge barrier for some farmers [212]. Third, the ethical issue regarding the use of AI in agricultural production has gained great interest over the past few years, which can be related to concerns about issues derived from inadequate design and setup of intelligent systems, which could lead to negative outcomes in agricultural production, such as violations of farmers’ privacy and harm to animal welfare due to the incorporation of robotic technologies, among others [213,214,215]. In this sense, the increased reliance on digital technology introduces new vulnerabilities, such as susceptibility to cyberattacks, including data breaches and unauthorized system manipulation, that could compromise the data and have severe consequences for individual farmers and the broader food supply [216,217,218]. Therefore, while AI-based decision support systems hold immense promise for optimizing cultivation parameters in soilless agriculture, they also present challenges related to cost, data quality, expertise, and security that must be critically evaluated.

7. Environmental Sustainability and Resource Management

7.1. Role of Next-Generation Technologies in Enhancing Sustainability

Next-generation technologies, including AI, real-time monitoring systems, precision agriculture, and predictive analytics, as detailed in Table 5, promise to enhance sustainability in soilless vegetable production. These technologies can optimize resource and energy use, reduce waste, and improve yield, contributing to economic and environmental sustainability [219]. For example, new applications of existing technologies, such as AI and mathematical models, have been successfully applied to predict energy consumption in agricultural applications, facilitating the evaluation of each production component and reducing CO2 emissions [220]. Additionally, real-time monitoring can facilitate precise irrigation and nutrient delivery, minimizing water and fertilizer use, such as in [221], where they developed an irrigation control system for soilless culture based on a proportional integral derivative (PID) algorithm that allows fully automatic operation with a minimum set of variables, calculating the average daily leaching fractions reasonably well, to reduce the cost. In [222], a review of IoT implementations for real-time monitoring, control, and management in aquaponics (a closed-loop soilless farming method) is presented. The importance of monitoring water variables is highlighted, such as water level, temperature, pH, dissolved oxygen, ammonia, nitrification, nitrites, electrical conductivity, total dissolved solids, salinity, and water hardness, and environmental variables, such as light intensity, relative humidity, air temperature, media moisture, and CO2. In addition, the study discusses wireless monitoring with the integration of sensors and communication technologies and the implementation of machine learning techniques.
A key point to precise irrigation in this closed system is the estimation of crop evapotranspiration (ETc). In this regard, ref. [223] generated artificial neural networks (ANN), utilizing climate data from sensors (air temperature, relative humidity, and solar radiation) and calculated inputs (crop coefficient and net radiation) and inputs from time and space (day of year, days after planting, and extraterrestrial radiation), to create models for ETc estimation for tomato (cv. Duru) plants grown in two substrates (perlite and coco fiber) in an unheated plastic greenhouse. When the models generated were compared to traditional methodologies, such as FAO Penman–Monteith (PM) and Hargreaves (HG) equations, it was found that even with limited data, ANN models predicted ETc better than classic equations, requiring only temperature and humidity sensors. This is because, in certain climatic conditions, PM and HG equations can overestimate or underestimate ETc, as has been evidenced by several authors [224,225,226,227,228,229,230,231,232,233]. Searching for similar results, ref. [234] followed a dynamic Bayesian approach to modeling crop coefficient (Kr) and predicting crop ET for greenhouse sweet basil grown in a soilless substrate, using input data from sensors measuring temperature, global radiation, and crop weight connected to a lightweight IoT client operating in real-time. The results showed that the Kr approach predicts crop evapotranspiration with much higher accuracy than the Baille-based ET (a simplified version of the PM equation proposed by [235] for greenhouses) approach.
Table 5. Overview of the Role of Next-Generation Technologies in Enhancing Sustainability.
Table 5. Overview of the Role of Next-Generation Technologies in Enhancing Sustainability.
Next-Gen TechnologiesAdvantages for SustainabilityPotential DrawbacksKey Areas of ImpactReferences
AI and Machine
Resource optimization,
waste reduction
Energy consumption, data privacyWater and nutrient
IoT DevicesReal-time monitoring, energy efficiencySecurity risks, e-wasteClimate control, irrigation[240,241,242,243]
BlockchainTraceability, transparent
supply chain
Complexity, scalability issuesFood safety, environmental
Renewable Energy SourcesLow carbon footprint,
long-term cost savings
Initial setup cost,
Energy supply for systems[11,249,250,251,252]
Drones and
Reduced labor, precision
High initial cost,
regulatory hurdles
Planting, harvesting,
However, there are also potential limitations that need to be addressed in the context of soilless farming. A recurring problem with these technologies is the high initial cost of implementation [257,258]. Additionally, accessibility to these technologies in rural areas may pose challenges because communication infrastructure (cellular connectivity, internet) is not always available [259]. When these technologies are implemented in real operating environments, their integration can be complex because interoperability is not well established, mainly because an area is still in development with much research being conducted in parallel. Therefore, ensuring seamless integration and interoperability among these technologies is crucial for their effective functioning [260,261]. The energy consumption of platforms where AI, machine learning, and communications technologies operate can be a limitation, especially in remote or off-grid farming locations [262]. This can impact the sustainability of smart farming systems, particularly when considering renewable energy sources. Additionally, renewable energy sources suffer from intermittency. For example, solar power generation is affected by factors such as time of day, weather conditions, and seasonal changes, so energy storage technologies, such as batteries, are a necessity [263]. AI and machine learning models depend heavily on data quality; hence, inaccurate or low-quality data can lead to incorrect decisions and recommendations in smart farming systems [209,264]. These data-intensive systems are susceptible to security breaches and privacy concerns among farmers [265], as was discussed previously. Solutions include ensuring diverse and high-quality datasets, developing explainable AI models, investing in secure communication protocols, addressing intermittency with energy storage, and investing in research to make this technology more available. Additionally, there is a need for government support, subsidies, and education programs to make these technologies more accessible to farmers, promoting widespread adoption in the agricultural sector [266].

7.2. Efficient Water and Nutrient Management Strategies

Efficient water and nutrient management strategies are pivotal in soilless vegetable production, offering the dual benefits of resource conservation and improved crop yield, emphasizing the environmental benefits of using inorganic substrate (rock wool and volcanic tuff) and the reuse of nutrient solution (closed systems) to avoid water and nutrient losses [267]. Additionally, advanced sensing technologies, including smart irrigation systems and the utilization of surface reflectance data obtained from crop canopies, offer promising avenues for enhancing the efficiency of soilless farming systems. Utilizing cloud-connected wireless sensor networks to monitor real-time substrate water status in basil [87] was able to automate irrigation and allowed assessment of the physiological responses of plants to different water availability levels in the growing substrate. For efficient crop management, detecting early signs of water deficit stress is crucial. In this sense, non-contact techniques for detecting changes in spectral reflectance have been used successfully for monitoring the water status of crops, as in tomatoes, where [239] found that crop reflectance increased with increasing water deficit or in [268], where spectral reflectance indices (SRI) allowed estimating the midday stem water potential in grapevines with great success. This demonstrates that smart irrigation systems are valuable instruments for precision irrigation management in soilless crop cultivation within greenhouses. When coupled with precise data regarding the impact of different water availability levels on plant growth, these systems offer a powerful solution for optimizing crop irrigation.
These precision agricultural techniques rely on real-time assessments of crop water demands rather than adhering to rigid watering schedules. As a result, they hold significant potential for optimizing water and nutrient utilization within soilless farming setups [267]. Technologies such as drip irrigation systems and nutrient film techniques can significantly reduce water usage, while real-time monitoring and AI-driven decision support systems can optimize nutrient delivery [267,269,270,271,272]. Traditional EC and pH sensors provide insufficient information about ion imbalances in hydroponic solutions, which can lead to nutrient wastage or reduced yields. Therefore, the use of on-site ion monitoring systems based on ion-selective electrodes (ISEs) that can automatically calibrate sensors and measure the concentrations of individual ions (NO3, K+, and Ca2+) in hydroponic solutions [271] allows farmers to effectively oversee nutrient management in reused solutions by promptly identifying any imbalances that may arise in the nutrient ratios has been proposed.
However, these advancements are not without challenges. The initial cost of implementing these technologies can be prohibitive for small-scale farmers, raising concerns about their access [273,274]. Moreover, the effectiveness of these strategies is highly dependent on accurate data and specialized expertise for system calibration and maintenance, which could be barriers for some growers. There is also the risk of over-reliance on technology, potentially reducing manual monitoring and adjustments vigilance. Other considerations also come into play, particularly regarding data privacy and ownership, as many of these systems require cloud-based data storage and third-party analytics platforms [275]. Additionally, while these strategies aim to be sustainable, the energy requirements for operating advanced water and nutrient management systems could offset some environmental benefits if the energy is sourced from non-renewable resources [276,277].

7.3. Energy-Saving Techniques in Soilless Vegetable Production

Energy-saving techniques, such as solar panels, energy-efficient lighting, and automated climate control systems, are increasingly integrated into soilless vegetable production to reduce operational costs and environmental impact. These technologies can significantly lower energy consumption and increase performance, thereby contributing to the sustainability of soilless farming practices [278,279,280,281,282]. The majority of current greenhouses utilize conventional materials on the facade and traditional technologies for heating, cooling, ventilation, air-conditioning, lighting, energy generation, and storage; therefore, by simply changing the design and materials of the greenhouse with novel energy-efficient, low-cost, and eco-friendly solutions, such as semi-transparent PV modules, vertical ground heat exchangers, solar assisted heat pump systems, windcatchers, vacuum tube windows, blue and red LEDs for lighting, among others, farmers can minimize their cost of cultivation and, thus, maximize their profits, with payback periods from 4 to 8 years [280].
However, adopting these energy-saving techniques is fraught with challenges that warrant critical analysis. Cost: the initial investment required for these technologies can be substantial, making it difficult for small-scale or financially constrained farmers to adopt them. Applicability: the effectiveness of these energy-saving techniques often depends on geographic and climatic factors; for example, solar panels may be less effective in regions with limited sunlight [283]. Reconversion: the transition to energy-efficient systems may require a reconfiguration of existing setups, which could be both labor-intensive and technically challenging. Specialized knowledge: while these techniques aim to reduce energy consumption, they often require a certain level of technical expertise for installation and maintenance [284,285], potentially creating a barrier for less tech-savvy farmers. Lastly, sustainability: the push for energy efficiency should be balanced with other sustainability considerations, such as water use and waste management, which are equally critical in soilless vegetable production [286,287].

7.4. Life Cycle Assessment and Eco-Friendly Practices

Life cycle assessment (LCA) and eco-friendly practices are becoming increasingly important in soilless vegetable production to evaluate the environmental impact of various cultivation methods and technologies. LCA provides a comprehensive environmental footprint analysis, from resource extraction to waste management, offering valuable insights into areas for improvement [288,289,290,291,292,293]. Eco-friendly practices, such as using organic substrates and renewable energy sources, aim to minimize this footprint. For example, ref. [289], reviewing substrates for use in urban farming, found that perlite has the highest environmental impact because it is a material obtained from open-pit mines, which necessitates substantial energy consumption and a lengthy transportation process. Moreover, horticultural and fruit crops are typically regarded as highly intensive and frequently require numerous inputs, including fertilizers, pesticides, and various other materials, and the benefit of one cropping system over another cannot always be established, as was described by [292] in strawberry cultivated in a traditional mulched soil tunnel versus a soilless tunnel system, concluding that, in general terms, the tunnel exploits more land and crop inputs (excluding pesticides), while the soilless, more technology and building materials, thus, making it difficult to establish which system is more environmentally sustainable.
As stated, these analyses can be challenging, especially for smallholder farmers. Conducting a thorough LCA can be resource-intensive, requiring specialized expertise and access to extensive data, which may be prohibitive or nonexistent. Eco-friendly practices often involve an upfront investment, raising concerns about cost and access to sustainable technologies. While these practices aim to be environmentally sustainable, their effectiveness can vary depending on local conditions such as climate and soil quality, which may limit their applicability on a broader scale. The transition to eco-friendly practices may necessitate significant changes to existing systems and operations, which could be costly and disruptive.

8. Future Directions and Challenges

8.1. Promising Areas for Further Research and Development

In the context of soilless vegetable production, promising areas for further research and development include the integration of AI and machine learning for predictive analytics, developing more efficient and affordable sensing technologies, and exploring alternative sustainable substrates [65]. These areas hold significant potential for advancing the field by improving yield, reducing resource consumption, and enhancing sustainability [294,295,296,297]. However, several challenges and considerations accompany these promising avenues. First, focusing on high-tech solutions like AI could divert attention and resources from low-tech but effective traditional practices, potentially widening the gap between large-scale and small-scale farmers [298]. Soilless farming, particularly in the context of traditional crops like rice, soybean, and wheat, has seen limited exploration when integrating new technologies, e.g., artificial intelligence. Surprisingly, there is a lack of research dedicated to harnessing the potential of AI in these basic crop cultivation methods. Many existing systems remain in the prototype stage, and their practical application in real-world agricultural settings remains largely conceptual, especially within the confines of greenhouse environments [219]. Second, while the push for more sustainable substrates is commendable, the long-term impacts of these materials on plant health and yield are not yet fully understood and warrant further investigation [65]. Third, the drive for innovation must be balanced with cost considerations and accessibility to ensure that advancements benefit a broad range of producers, including those with limited resources [299,300]. Fourth, as research delves into increasingly specialized areas, there is a risk of creating solutions that are so tailored that they cannot be easily adapted for broader applications. Finally, all new technologies and practices must be evaluated for their immediate benefits and long-term sustainability, including their environmental, social, and economic impacts [301].

8.2. Regulatory and Policy Considerations for Next-Generation Technologies

In the context of the rapid advancement of next-generation technologies in soilless vegetable production, “data and intellectual property of farmers” takes on a multifaceted dimension that intersects with privacy, security, and economic interests [302]. As we integrate sophisticated AI and real-time monitoring systems into agricultural practices, these systems invariably collect and process vast data points [303,304]. These data, ranging from crop health metrics to environmental conditions within soilless systems, constitute an asset central to operational success and innovation in modern farming. Farmers’ intellectual property extends beyond the traditional understanding of crop varieties or farming techniques. It now includes the unique datasets and proprietary algorithms developed to interpret these data, optimize production, and manage resources efficiently. The data collected through these advanced systems can reveal insights into the micro-level responses of crops to various stimuli, which, when analyzed, contribute to the macro-level strategies for improving yield and sustainability. However, the aggregation and analysis of such data also raise significant concerns regarding ownership and control. Who has the right to access, analyze, and benefit from these data? How can farmers protect their data from being exploited by larger entities that can process and utilize this information at a scale far beyond the individual farmer’s capability? These questions underscore the need for robust regulatory frameworks that recognize the value of these data and the intellectual property they generate [302]. Regulatory guidelines must be crafted to ensure that farmers retain control over their data and their intellectual contributions to soilless agriculture. This involves establishing clear data governance policies that address consent, access rights, and the distribution of benefits derived from data use.
Moreover, these guidelines must safeguard against unauthorized use of data, prevent data breaches, and ensure that farmers’ data are not used in ways that could harm their competitive position or violate their privacy [303,304]. As we consider integrating new materials, energy sources, and potentially genetically modified crops into soilless farming systems, the scope of intellectual property also expands. It now encompasses the innovative use of alternative substrates, the development of crops specifically tailored to soilless conditions, and the creation of new technologies for energy efficiency [305,306]. Each area brings with it the need for updated safety and environmental impact assessments and the potential for new patents and trademarks. Standardization efforts are critical to ensure that these new technologies can be seamlessly integrated with existing systems, but they must be balanced to protect farmers’ intellectual property rights [307]. Without such protections, there is a risk that the benefits of innovation will accrue only to those with the resources to exploit them, potentially widening the gap between large-scale operations and smaller, resource-constrained farmers.
Furthermore, access to technology is a pressing concern [303]. Policies must be designed to ensure that all farmers, regardless of scale, can benefit from technological advancements. This includes considering the potential impacts of technology adoption, such as job displacement due to automation [308]. Policy frameworks must protect data and intellectual property and address the socioeconomic impacts of technology adoption, ensuring that the transition to more advanced systems is just and inclusive.

8.3. Potential Limitations and Hurdles to Widespread Adoption

Several limitations temper the potential for widespread adoption of next-generation technologies in soilless vegetable production (Table 6). First and foremost is the cost issue; the initial investment for implementing advanced technologies such as AI-driven monitoring systems, precision farming techniques, new materials, and alternative substrates can be prohibitively expensive for small-scale farmers. This financial barrier risks creating a technological divide in the industry, where only well-funded operations can afford to adopt these innovations [309]. Second, there is the challenge of technical complexity; the operation and maintenance of advanced systems often require specialized skills and training, which could be a significant hurdle for less tech-savvy farmers [310]. Third, the effectiveness of these technologies can be influenced by various external factors such as climate, geographic location, and market demand, limiting their applicability in certain contexts [311]. Fourth, regulatory considerations, including data privacy and potential job displacement [303,312], could slow down the rate of adoption. Fifth, if not sourced sustainably, the energy requirements for some of these technologies could offset the environmental benefits they aim to provide [313,314]. Lastly, there is the issue of social acceptance; farmers and consumers may be hesitant to embrace these technologies due to concerns about safety or data security considerations [315].
To address the limitations and hurdles to the widespread adoption of soilless agriculture, several strategic approaches and solutions can be considered, such as providing farmers with the necessary knowledge and training to adopt soilless agriculture techniques effectively through education [333,334]. Providing financial incentives such as grants, subsidies, and tax breaks for farmers and businesses to adopt soilless agriculture can help offset initial setup costs and encourage investment in this method, especially for smallholder farmers [335,336]. Developing supportive policies and regulations that recognize and promote soilless agriculture can create an enabling environment for its adoption [337]. This includes zoning regulations, water usage policies, and agricultural subsidies designed to support soilless farming.
The introduction of automation in farming systems can optimize power consumption. By introducing automated systems and technologies, farmers can reduce energy consumption while maintaining or even increasing yield efficiency [338]. The aforementioned, together with the integration of alternative power, such as renewable energy sources, allows for the reduction of energy consumption and environmental impact. Research has shown that soilless cultivation optimally utilizes water, fertilizer, and land resources, achieving efficiencies of 90%, 70%, and 75%, respectively. Soilless agriculture uses water more efficiently than traditional soil-based farming, as the water and nutrient solutions are recirculated, reducing overall water consumption. By eliminating soil disturbance, it alleviates land pressure and degradation while facilitating year-round crop intensification. This method complements traditional soil-based farming, diminishes the adverse effects of agrochemicals on the environment, addresses climate change, and enhances productivity in arid regions [5]. Educating farmers, agricultural communities, and the general public about these benefits and techniques of soilless agriculture is crucial for its social acceptance [339]. This can be achieved through workshops, seminars, and educational campaigns. Highlighting the advantages, such as higher yields, efficient water usage, and reduced environmental impact, can help increase awareness and understanding.

9. Conclusions and Final Remarks

This research paper provides a thorough and balanced investigation into the role of next-generation technologies in soilless vegetable production, emphasizing both their revolutionary capabilities and the multifaceted challenges that limit their widespread adoption. It addresses the financial barriers that could create a technological divide, favoring well-funded operations over small-scale farmers. The paper also critically examines the technical complexities that necessitate specialized skills, thereby widening the existing knowledge gap among farmers. While the paper is strong in discussing potential impacts, such as data privacy and job displacement, it could benefit from a more in-depth exploration of the regulatory frameworks and international trade regulations that may need to be revised to accommodate these innovations. Ongoing work in this field should prioritize the development of cost-effective technologies and educational programs to make these advancements more accessible. Future research should also focus on the long-term environmental sustainability of these technologies, as well as a more granular understanding of their implications. By addressing these critical areas, this research can contribute to a more holistic understanding of the challenges and opportunities, facilitating the implementation of next-generation technologies in soilless vegetable production.

Author Contributions

F.F.-P.: Supervision, Conceptualization, Formal analysis, Writing—original draft. G.C.S.: Writing—review and editing. K.G.: Conceptualization, Investigation, Writing—original draft. R.V.: Investigation, Formal analysis, Writing—review and editing. All authors have read and agreed to the published version of the manuscript.


This study was supported by the Chilean government through the projects Nodo CTCI MCS-ANID-NODO220006, ANID (REDES-FOVI220031), ANID-Subdirección de Capital Humano, Doctorado Nacional 2021 Folio 21212122 (K.Gutter) and 21211937 (R.Vega), FIC (No. BIP 40.036.334-0) and International Initiative for Digitalization in Agriculture (IIDA).

Data Availability Statement

Not applicable.


The authors of this research thank the Corporación de Tecnologías Avanzadas para la Agricultura (CTAA) and the Consorcio Sur-Subantártico Ciencia 2030.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Hernández-Martínez, N.R.; Blanchard, C.; Wells, D.; Salazar-Gutiérrez, M.R. Current State and Future Perspectives of Commercial Strawberry Production: A Review. Sci. Hortic. 2023, 312, 111893. [Google Scholar] [CrossRef]
  2. Atzori, G.; Mancuso, S.; Masi, E. Seawater Potential Use in Soilless Culture: A Review. Sci. Hortic. 2019, 249, 199–207. [Google Scholar] [CrossRef]
  3. Tzortzakis, N.; Nicola, S.; Savvas, D.; Voogt, W. Editorial: Soilless Cultivation Through an Intensive Crop Production Scheme. Management Strategies, Challenges and Future Directions. Front. Plant Sci. 2020, 11, 363. [Google Scholar] [CrossRef] [PubMed]
  4. Massa, D.; Magán, J.J.; Montesano, F.F.; Tzortzakis, N. Minimizing Water and Nutrient Losses from Soilless Cropping in Southern Europe. Agric. Water Manag. 2020, 241, 106395. [Google Scholar] [CrossRef]
  5. Gebereegziher, W.G. Soilless Culture Technology to Transform Vegetable Farming, Reduce Land Pressure and Degradation in Drylands. Cogent Food Agric. 2023, 9, 2265106. [Google Scholar] [CrossRef]
  6. Fussy, A.; Papenbrock, J. An Overview of Soil and Soilless Cultivation Techniques—Chances, Challenges and the Neglected Question of Sustainability. Plants 2022, 11, 1153. [Google Scholar] [CrossRef] [PubMed]
  7. Gonnella, M.; Renna, M. The Evolution of Soilless Systems towards Ecological Sustainability in the Perspective of a Circular Economy. Is It Really the Opposite of Organic Agriculture? Agronomy 2021, 11, 950. [Google Scholar] [CrossRef]
  8. Carrasco, G.; Fuentes-Penailillo, F.; Perez, R.; Rebolledo, P.; Manriquez, P. An Approach to a Vertical Farming Low-Cost to Reach Sustainable Vegetable Crops. In Proceedings of the 2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control: For the Development of Sustainable Agricultural Systems, ICA-ACCA 2022, Curicó, Chile, 24–28 October 2022; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2022. [Google Scholar]
  9. Fuentes-Penailillo, F.; Ortega-Farias, S.; Tian, F.; Perez, R.; Calderon, V.; Perez, D. Towards the Monitoring of Water Consumption of Crops Using Digital Agriculture Techniques. In Proceedings of the 2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control: For the Development of Sustainable Agricultural Systems, ICA-ACCA 2022, Curicó, Chile, 24–28 October 2022; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2022. [Google Scholar]
  10. Gutter, K.; Ortega-Farías, S.; Fuentes-Penailillo, F.; Moreno, M.; Vega-Ibánez, R.; Riveros-Burgos, C.; Albornoz, J. Estimation of Vineyard Water Status Using Infrared Thermometry Measured at Two Positions of the Canopy. Acta Hortic. 2022, 1335, 331–337. [Google Scholar] [CrossRef]
  11. Carrasco, G.; Manrıquez, P.; Galleguillos, F.; Fuentes-Peñailillo, F.; Urrestarazu, M. Evolution of Soilless Culture in Chile. Acta Hortic. 2021, 1321, 267–273. [Google Scholar] [CrossRef]
  12. Gumisiriza, M.S.; Ndakidemi, P.; Nalunga, A.; Mbega, E.R. Building Sustainable Societies through Vertical Soilless Farming: A Cost-Effectiveness Analysis on a Small-Scale Non-Greenhouse Hydroponic System. Sustain. Cities Soc. 2022, 83, 103923. [Google Scholar] [CrossRef]
  13. Kläring, H.-P. Strategies to Control Water and Nutrient Supplies to Greenhouse Crops. A Review. Agronomie 2001, 21, 311–321. [Google Scholar] [CrossRef]
  14. Resh, H. Hydroponic Food Production a Definitive Guidebook for the Advanced Home Gardener and the Commercial Hydroponic Grower, 8th ed.; CRC Press: Boca Raton, FL, USA, 2022; ISBN 978-0-367-67822-7. [Google Scholar]
  15. Cowan, N.; Ferrier, L.; Spears, B.; Drewer, J.; Reay, D.; Skiba, U. CEA Systems: The Means to Achieve Future Food Security and Environmental Sustainability? Front. Sustain. Food Syst. 2022, 6, 891256. [Google Scholar] [CrossRef]
  16. Ragaveena, S.; Shirly Edward, A.; Surendran, U. Smart Controlled Environment Agriculture Methods: A Holistic Review. Rev. Env. Sci. Biotechnol. 2021, 20, 887–913. [Google Scholar] [CrossRef]
  17. Hati, A.J.; Singh, R.R. AI-Driven Pheno-Parenting: A Deep Learning Based Plant Phenotyping Trait Analysis Model on a Novel Soilless Farming Dataset. IEEE Access 2023, 11, 35298–35314. [Google Scholar] [CrossRef]
  18. Gumisiriza, M.S.; Kabirizi, J.M.L.; Mugerwa, M.; Ndakidemi, P.A.; Mbega, E.R. Can Soilless Farming Feed Urban East Africa? An Assessment of the Benefits and Challenges of Hydroponics in Uganda and Tanzania. Environ. Chall. 2022, 6, 100413. [Google Scholar] [CrossRef]
  19. D’Amico, A.; De Boni, A.; Ottomano Palmisano, G.; Acciani, C.; Roma, R. Environmental Analysis of Soilless Tomato Production in a High-Tech Greenhouse. Clean. Environ. Syst. 2023, 11, 100137. [Google Scholar] [CrossRef]
  20. Wittmann, S.; Jüttner, I.; Mempel, H. Indoor Farming Marjoram Production—Quality, Resource Efficiency, and Potential of Application. Agronomy 2020, 10, 1769. [Google Scholar] [CrossRef]
  21. Gupta, M.; Abdelsalam, M.; Khorsandroo, S.; Mittal, S. Security and Privacy in Smart Farming: Challenges and Opportunities. IEEE Access 2020, 8, 34564–34584. [Google Scholar] [CrossRef]
  22. Rotz, S.; Duncan, E.; Small, M.; Botschner, J.; Dara, R.; Mosby, I.; Reed, M.; Fraser, E.D.G. The Politics of Digital Agricultural Technologies: A Preliminary Review. Sociol. Rural. 2019, 59, 203–229. [Google Scholar] [CrossRef]
  23. Lajoie-O’Malley, A.; Bronson, K.; van der Burg, S.; Klerkx, L. The Future(s) of Digital Agriculture and Sustainable Food Systems: An Analysis of High-Level Policy Documents. Ecosyst. Serv. 2020, 45, 101183. [Google Scholar] [CrossRef]
  24. Windsor, G.W.; Schwarz, M. Soilless Culture for Horticultural Crop Production; FAO Plant Production and Protection Paper; 101; Food and Agriculture Organization of the United Nations: Rome, Italy, 1990; ISBN 9251029520. [Google Scholar]
  25. Szekely, I.; Jijakli, M.H. Bioponics as a Promising Approach to Sustainable Agriculture: A Review of the Main Methods for Producing Organic Nutrient Solution for Hydroponics. Water 2022, 14, 3975. [Google Scholar] [CrossRef]
  26. Bliedung, A.; Dockhorn, T.; Germer, J.; Mayerl, C.; Mohr, M. Experiences of Running a Hydroponic System in a Pilot Scale for Resource-Efficient Water Reuse. J. Water Reuse Desalination 2020, 10, 347–362. [Google Scholar] [CrossRef]
  27. Stegelmeier, A.A.; Rose, D.M.; Joris, B.R.; Glick, B.R. The Use of PGPB to Promote Plant Hydroponic Growth. Plants 2022, 11, 2783. [Google Scholar] [CrossRef]
  28. Nguyen, N.T.; McInturf, S.A.; Mendoza-Cózatl, D.G. Hydroponics: A Versatile System to Study Nutrient Allocation and Plant Responses to Nutrient Availability and Exposure to Toxic Elements. J. Vis. Exp. 2016, 2016, e54317. [Google Scholar] [CrossRef]
  29. Sharma, N.; Acharya, S.; Kumar, K.; Singh, N.; Chaurasia, O.P. Hydroponics as an Advanced Technique for Vegetable Production: An Overview. J. Soil. Water Conserv. 2018, 17, 364. [Google Scholar] [CrossRef]
  30. Cooper, A. The ABC of NFT: Nutrient Film Technique; Grower Book: London, UK, 1979. [Google Scholar]
  31. Nursyahid, A.; Setyawan, T.A.; Sa’diyah, K.; Wardihani, E.D.; Helmy, H.; Hasan, A. Analysis of Deep Water Culture (DWC) Hydroponic Nutrient Solution Level Control Systems. IOP Conf. Ser. Mater. Sci. Eng. 2021, 1108, 012032. [Google Scholar] [CrossRef]
  32. Cai, J.; Veerappan, V.; Arildsen, K.; Sullivan, C.; Piechowicz, M.; Frugoli, J.; Dickstein, R. A Modified Aeroponic System for Growing Small-Seeded Legumes and Other Plants to Study Root Systems. Plant Methods 2023, 19, 21. [Google Scholar] [CrossRef]
  33. Urrestarazu, M.; Carrasco Silva, G. Soiless Culture and Hydroponics; Mundi Prensa Libros S.A: Madrid, Spain, 2023; ISBN 978-84-8476-766-4. [Google Scholar]
  34. Quy, V.K.; Van Hau, N.; Anh, D.V.; Quy, N.M.; Ban, N.T.; Lanza, S.; Randazzo, G.; Muzirafuti, A. IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Appl. Sci. 2022, 12, 3396. [Google Scholar] [CrossRef]
  35. Alahi, M.E.E.; Sukkuea, A.; Tina, F.W.; Nag, A.; Kurdthongmee, W.; Suwannarat, K.; Mukhopadhyay, S.C. Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends. Sensors 2023, 23, 5206. [Google Scholar] [CrossRef]
  36. Monostori, I.; Heilmann, M.; Kocsy, G.; Rakszegi, M.; Ahres, M.; Altenbach, S.B.; Szalai, G.; Pál, M.; Toldi, D.; Simon-Sarkadi, L.; et al. LED Lighting–Modification of Growth, Metabolism, Yield and Flour Composition in Wheat by Spectral Quality and Intensity. Front. Plant Sci. 2018, 9, 605. [Google Scholar] [CrossRef]
  37. Holzinger, A.; Keiblinger, K.; Holub, P.; Zatloukal, K.; Müller, H. AI for Life: Trends in Artificial Intelligence for Biotechnology. N. Biotechnol. 2023, 74, 16–24. [Google Scholar] [CrossRef] [PubMed]
  38. Kootstra, G.; Wang, X.; Blok, P.; Hemming, J.; van Henten, E. Selective Harvesting Robotics: Current Research, Trends, and Future Directions. Agric. Robot. 2021, 2, 95–104. [Google Scholar] [CrossRef]
  39. Clyde-Smith, D.; Campos, L.C. Engineering Hydroponic Systems for Sustainable Wastewater Treatment and Plant Growth. Appl. Sci. 2023, 13, 8032. [Google Scholar] [CrossRef]
  40. Aishwarya, J.M.; Vidhya, R. Study on the Efficiency of a Hydroponic Treatment for Removing Organic Loading from Wastewater and Its Application as a Nutrient for the “Amaranthus Campestris” Plant for Sustainability. Sustainability 2023, 15, 7814. [Google Scholar] [CrossRef]
  41. Tatas, K.; Al-Zoubi, A.; Christofides, N.; Zannettis, C.; Chrysostomou, M.; Panteli, S.; Antoniou, A. Reliable IoT-Based Monitoring and Control of Hydroponic Systems. Technologies 2022, 10, 26. [Google Scholar] [CrossRef]
  42. Sneineh, A.A.; Shabaneh, A.A.A. Design of a Smart Hydroponics Monitoring System Using an ESP32 Microcontroller and the Internet of Things. MethodsX 2023, 11, 102401. [Google Scholar] [CrossRef] [PubMed]
  43. Vanipriya, C.H.; Maruyi; Malladi, S.; Gupta, G. Artificial Intelligence Enabled Plant Emotion Xpresser in the Development Hydroponics System. Mater. Today Proc. 2021, 45, 5034–5040. [Google Scholar] [CrossRef]
  44. Lowe, M.; Qin, R.; Mao, X. A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring. Water 2022, 14, 1384. [Google Scholar] [CrossRef]
  45. 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]
  46. Kour, K.; Gupta, D.; Gupta, K.; Anand, D.; Elkamchouchi, D.H.; Pérez-Oleaga, C.M.; Ibrahim, M.; Goyal, N. Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation. Sensors 2022, 22, 8905. [Google Scholar] [CrossRef]
  47. Herman; Surantha, N. Intelligent Monitoring and Controlling System for Hydroponics Precision Agriculture. In Proceedings of the 2019 7th International Conference on Information and Communication Technology, ICoICT 2019, Kuala Lumpur, Malaysia, 24–26 July 2019; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2019. [Google Scholar]
  48. Karamanis, G.; Drosos, C.; Papoutsidakis, M.; Tseles, D.; Drosos, C.; Papoutsidakis, M.; Tseles, D. Implementation of an Automated System for Controlling and Monitoring a Hydroponic Greenhouse. Int. J. Eng. Sci. Invent. 2018, 7, 27–35. [Google Scholar]
  49. Li, Y.; Liu, N.; Ji, F.; He, D. Optimal Red:Blue Ratio of Full Spectrum LEDs for Hydroponic Pakchoi Cultivation in Plant Factory. Int. J. Agric. Biol. Eng. 2022, 15, 72–77. [Google Scholar] [CrossRef]
  50. Nguyen, T.K.L.; Cho, K.M.; Lee, H.Y.; Sim, H.S.; Kim, J.H.; Son, K.H. Growth, Fruit Yield, and Bioactive Compounds of Cherry Tomato in Response to Specific White-Based Full-Spectrum Supplemental LED Lighting. Horticulturae 2022, 8, 319. [Google Scholar] [CrossRef]
  51. Rukhiran, M.; Netinant, P. Mobile Application Development of Hydroponic Smart Farm Using Information Flow Diagram. In Proceedings of the National Conference on Information Technology, Chonburi, Thailand, 21–22 October 2020. [Google Scholar]
  52. Tiglao, N.M.; Alipio, M.; Balanay, J.V.; Saldivar, E.; Tiston, J.L. Agrinex: A Low-Cost Wireless Mesh-Based Smart Irrigation System. Measurement 2020, 161, 107874. [Google Scholar] [CrossRef]
  53. Alipio, M.I.; Dela Cruz, A.E.M.; Doria, J.D.A.; Fruto, R.M.S. On the Design of Nutrient Film Technique Hydroponics Farm for Smart Agriculture. Eng. Agric. Environ. Food 2019, 12, 315–324. [Google Scholar] [CrossRef]
  54. Benke, K.; Tomkins, B. Future Food-Production Systems: Vertical Farming and Controlled-Environment Agriculture. Sustain. Sci. Pract. Policy 2017, 13, 13–26. [Google Scholar] [CrossRef]
  55. Carotti, L.; Pistillo, A.; Zauli, I.; Meneghello, D.; Martin, M.; Pennisi, G.; Gianquinto, G.; Orsini, F. Improving Water Use Efficiency in Vertical Farming: Effects of Growing Systems, Far-Red Radiation and Planting Density on Lettuce Cultivation. Agric. Water Manag. 2023, 285, 108365. [Google Scholar] [CrossRef]
  56. Kennard, N.; Stirling, R.; Prashar, A.; Lopez-Capel, E. Evaluation of Recycled Materials as Hydroponic Growing Media. Agronomy 2020, 10, 1092. [Google Scholar] [CrossRef]
  57. Souza, V.; Gimenes, R.M.T.; de Almeida, M.G.; Farinha, M.U.S.; Bernardo, L.V.M.; Ruviaro, C.F. Economic Feasibility of Adopting a Hydroponics System on Substrate in Small Rural Properties. Clean. Technol. Env. Policy 2023, 25, 2761–2775. [Google Scholar] [CrossRef]
  58. Udovichenko, A.; Fleck, B.A.; Weis, T.; Zhong, L. Framework for Design and Optimization of a Retrofitted Light Industrial Space with a Renewable Energy-Assisted Hydroponics Facility in a Rural Northern Canadian Community. J. Build. Eng. 2021, 37, 102160. [Google Scholar] [CrossRef]
  59. Folorunso, E.A.; Schmautz, Z.; Gebauer, R.; Mraz, J. The Economic Viability of Commercial-Scale Hydroponics: Nigeria as a Case Study. Heliyon 2023, 9, e18979. [Google Scholar] [CrossRef] [PubMed]
  60. Martinez-Mate, M.A.; Martin-Gorriz, B.; Martínez-Alvarez, V.; Soto-García, M.; Maestre-Valero, J.F. Hydroponic System and Desalinated Seawater as an Alternative Farm-Productive Proposal in Water Scarcity Areas: Energy and Greenhouse Gas Emissions Analysis of Lettuce Production in Southeast Spain. J. Clean. Prod. 2018, 172, 1298–1310. [Google Scholar] [CrossRef]
  61. Cifuentes-Torres, L.; Mendoza-Espinosa, L.G.; Correa-Reyes, G.; Daesslé, L.W. Hydroponics with Wastewater: A Review of Trends and Opportunities. Water Environ. J. 2021, 35, 166–180. [Google Scholar] [CrossRef]
  62. Parkes, M.G.; Azevedo, D.L.; Domingos, T.; Teixeira, R.F.M. Narratives and Benefits of Agricultural Technology in Urban Buildings: A Review. Atmosphere 2022, 13, 1250. [Google Scholar] [CrossRef]
  63. Lubna, F.A.; Lewus, D.C.; Shelford, T.J.; Both, A.J. What You May Not Realize about Vertical Farming. Horticulturae 2022, 8, 322. [Google Scholar] [CrossRef]
  64. Gruda, N.S. Increasing Sustainability of Growing Media Constituents and Stand-Alone Substrates in Soilless Culture Systems. Agronomy 2019, 9, 298. [Google Scholar] [CrossRef]
  65. Sapkota, S.; Sapkota, S.; Liu, Z. Effects of Nutrient Composition and Lettuce Cultivar on Crop Production in Hydroponic Culture. Horticulturae 2019, 5, 72. [Google Scholar] [CrossRef]
  66. Schmautz, Z.; Loeu, F.; Liebisch, F.; Graber, A.; Mathis, A.; Bulc, T.G.; Junge, R. Tomato Productivity and Quality in Aquaponics: Comparison of Three Hydroponic Methods. Water 2016, 8, 533. [Google Scholar] [CrossRef]
  67. Tan, J.; Jiang, H.; Li, Y.; He, R.; Liu, K.; Chen, Y.; He, X.; Liu, X.; Liu, H. Growth, Phytochemicals, and Antioxidant Activity of Kale Grown under Different Nutrient-Solution Depths in Hydroponic. Horticulturae 2023, 9, 53. [Google Scholar] [CrossRef]
  68. Lateef, A.; Nazir, R.; Jamil, N.; Alam, S.; Shah, R.; Khan, M.N.; Saleem, M. Synthesis and Characterization of Zeolite Based Nano-Composite: An Environment Friendly Slow Release Fertilizer. Microporous Mesoporous Mater. 2016, 232, 174–183. [Google Scholar] [CrossRef]
  69. Rombel, A.; Krasucka, P.; Oleszczuk, P. Sustainable Biochar-Based Soil Fertilizers and Amendments as a New Trend in Biochar Research. Sci. Total Environ. 2022, 816, 151588. [Google Scholar] [CrossRef]
  70. Głąb, T.; Gondek, K.; Mierzwa–Hersztek, M. Biological Effects of Biochar and Zeolite Used for Remediation of Soil Contaminated with Toxic Heavy Metals. Sci. Rep. 2021, 11, 6998. [Google Scholar] [CrossRef]
  71. Singh Yadav, S.P.; Bhandari, S.; Bhatta, D.; Poudel, A.; Bhattarai, S.; Yadav, P.; Ghimire, N.; Paudel, P.; Paudel, P.; Shrestha, J.; et al. Biochar Application: A Sustainable Approach to Improve Soil Health. J. Agric. Food Res. 2023, 11, 100498. [Google Scholar] [CrossRef]
  72. Barrett, G.E.; Alexander, P.D.; Robinson, J.S.; Bragg, N.C. Achieving Environmentally Sustainable Growing Media for Soilless Plant Cultivation Systems—A Review. Sci. Hortic. 2016, 212, 220–234. [Google Scholar] [CrossRef]
  73. Machado, R.M.A.; Alves-Pereira, I.; Ferreira, R.; Gruda, N.S. Coir, an Alternative to Peat—Effects on Plant Growth, Phytochemical Accumulation, and Antioxidant Power of Spinach. Horticulturae 2021, 7, 127. [Google Scholar] [CrossRef]
  74. Munns, R. Plant Adaptations to Salt and Water Stress: Differences and Commonalities. Adv. Bot. Res. 2011, 57, 1–32. [Google Scholar] [CrossRef]
  75. Nerlich, A.; Dannehl, D. Soilless Cultivation: Dynamically Changing Chemical Properties and Physical Conditions of Organic Substrates Influence the Plant Phenotype of Lettuce. Front. Plant Sci. 2021, 11, 601455. [Google Scholar] [CrossRef] [PubMed]
  76. Kremenetskaya, I.; Ivanova, L.; Chislov, M.; Zvereva, I.; Vasilieva, T.; Marchevskaya, V.; Semushin, V.; Slukovskaya, M. Physicochemical Transformation of Expanded Vermiculite after Long-Term Use in Hydroponics. Appl. Clay Sci. 2020, 198, 105839. [Google Scholar] [CrossRef]
  77. EI-Kazzaz, A. Soilless Agriculture a New and Advanced Method for Agriculture Development: An Introduction. Agric. Res. Technol. Open Access J. 2017, 3, 555610. [Google Scholar] [CrossRef]
  78. Banitalebi, G.; Mosaddeghi, M.R.; Shariatmadari, H. Feasibility of Agricultural Residues and Their Biochars for Plant Growing Media: Physical and Hydraulic Properties. Waste Manag. 2019, 87, 577–589. [Google Scholar] [CrossRef]
  79. Joseph, S.; Cowie, A.L.; Van Zwieten, L.; Bolan, N.; Budai, A.; Buss, W.; Cayuela, M.L.; Graber, E.R.; Ippolito, J.A.; Kuzyakov, Y.; et al. How Biochar Works, and When It Doesn’t: A Review of Mechanisms Controlling Soil and Plant Responses to Biochar. GCB Bioenergy 2021, 13, 1731–1764. [Google Scholar] [CrossRef]
  80. Pandey, K.; Singh, K.G.; Singh, A. Multi-Sensors Based Smart Nutrient Reuse Management System for Closed Soilless Culture under Protected Cultivation. Comput. Electron. Agric. 2023, 204, 107495. [Google Scholar] [CrossRef]
  81. Ojha, T.; Misra, S.; Raghuwanshi, N.S. Wireless Sensor Networks for Agriculture: The State-of-the-Art in Practice and Future Challenges. Comput. Electron. Agric. 2015, 118, 66–84. [Google Scholar] [CrossRef]
  82. Avgoustaki, D.D.; Xydis, G. How Energy Innovation in Indoor Vertical Farming Can Improve Food Security, Sustainability, and Food Safety? In Advances in Food Security and Sustainability; Elsevier Ltd.: Amsterdam, The Netherlands, 2020; Volume 5, pp. 1–51. [Google Scholar]
  83. Preite, L.; Solari, F.; Vignali, G. Technologies to Optimize the Water Consumption in Agriculture: A Systematic Review. Sustainability 2023, 15, 5975. [Google Scholar] [CrossRef]
  84. Liang, Y.; Gao, G. Design and Analysis of Automatic Vegetable Harvesting Machine. In Proceedings of the 2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2019, Shanghai, China, 22–24 November 2019; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2019; pp. 287–290. [Google Scholar]
  85. Tavan, M.; Wee, B.; Brodie, G.; Fuentes, S.; Pang, A.; Gupta, D. Optimizing Sensor-Based Irrigation Management in a Soilless Vertical Farm for Growing Microgreens. Front. Sustain. Food Syst. 2021, 4, 622720. [Google Scholar] [CrossRef]
  86. Montesano, F.F.; van Iersel, M.W.; Boari, F.; Cantore, V.; D’Amato, G.; Parente, A. Sensor-Based Irrigation Management of Soilless Basil Using a New Smart Irrigation System: Effects of Set-Point on Plant Physiological Responses and Crop Performance. Agric. Water Manag. 2018, 203, 20–29. [Google Scholar] [CrossRef]
  87. Steidle Neto, A.J.; Zolnier, S.; de Carvalho Lopes, D. Development and Evaluation of an Automated System for Fertigation Control in Soilless Tomato Production. Comput. Electron. Agric. 2014, 103, 17–25. [Google Scholar] [CrossRef]
  88. Dordas, C. Role of Nutrients in Controlling Plant Diseases in Sustainable Agriculture. A Review. Agron. Sustain. Dev. 2008, 28, 33–46. [Google Scholar] [CrossRef]
  89. Ikrang, E.G.; Ehiomogue, P.O.; Udoumoh, U.I. Hydroponics in Precision Agriculture: A Review. Int. J. Eng. 2022, 20, 143–148. [Google Scholar]
  90. Chaiwongsai, J. Automatic Control and Management System for Tropical Hydroponic Cultivatio. In Proceedings of the 2019 IEEE International Symposium on Circuits and Systems, Sapporo, Japan, 26–29 May 2019. [Google Scholar]
  91. Ferrández-Pastor, F.J.; García-Chamizo, J.M.; Nieto-Hidalgo, M.; Mora-Pascual, J.; Mora-Martínez, J. Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture. Sensors 2016, 16, 1141. [Google Scholar] [CrossRef]
  92. Sánchez Millán, F.; Ortiz, F.J.; Mestre Ortuño, T.C.; Frutos, A.; Martínez, V. Development of Smart Irrigation Equipment for Soilless Crops Based on the Current Most Representative Water-Demand Sensors. Sensors 2023, 23, 3177. [Google Scholar] [CrossRef] [PubMed]
  93. Dhanaraju, M.; Chenniappan, P.; Ramalingam, K.; Pazhanivelan, S.; Kaliaperumal, R. Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agriculture 2022, 12, 1745. [Google Scholar] [CrossRef]
  94. Adeyemi, O.; Grove, I.; Peets, S.; Norton, T. Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation. Sustainability 2017, 9, 353. [Google Scholar] [CrossRef]
  95. Sobri, N.A.; Ahmad, I.; Maharum, S.M.; Mansor, Z.; Rahman, A.H.A.; Aziz, A.A. Development of Hydroponics System and Data Monitoring Using Internet of Things. In Proceedings of the 2022 IEEE 8th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), Melaka, Malaysia, 26–28 September 2022; pp. 345–349. [Google Scholar]
  96. Naphtali, J.H.; Misra, S.; Wejin, J.; Agrawal, A.; Oluranti, J. An Intelligent Hydroponic Farm Monitoring System Using IoT. In Data, Engineering and Applications; Sharma, S., Peng, S.-L., Agrawal, J., Shukla, R.K., Le, D.-N., Eds.; Springer Nature: Singapore, 2022; pp. 409–420. [Google Scholar]
  97. Christofi, A.; Margariti, G.; Salapatas, A.; Papageorgiou, G.; Zervas, P.; Karampiperis, P.; Koukourikos, A.; Tarantilis, P.A.; Kaparakou, E.H.; Misiakos, K.; et al. Determining the Nutrient Content of Hydroponically-Cultivated Microgreens with Immersible Silicon Determining the Nutrient Content of Hydroponically-Cultivated Microgreens with Immersible Silicon Photonic Sensors: A Preliminary Feasibility Study. Sensors 2023, 23, 5937. [Google Scholar] [CrossRef]
  98. Popkova, E.G. Case Study of Smart Innovation in Agriculture on the Example of a Vertical Farm. In Smart Innovation in Agriculture; Popkova Elena, G., Sergi, B.S., Eds.; Springer Nature: Singapore, 2022; pp. 303–309. ISBN 978-981-16-7633-8. [Google Scholar]
  99. Kangogo, D.; Dentoni, D.; Bijman, J. Adoption of Climate-smart Agriculture among Smallholder Farmers: Does Farmer Entrepreneurship Matter? Land Use Policy 2021, 109, 105666. [Google Scholar] [CrossRef]
  100. Kaur, G.; Upadhayaya, P.; Chawla, P. The Study of Sensors in Soil-Less Farming Techniques for Modern Agriculture. In Recent Advances in Intelligent Manufacturing; Harish, K., Jain, P.K., Goel, S., Eds.; Springer Nature: Singapore, 2023; pp. 293–307. [Google Scholar]
  101. Eridani, D.; Wardhani, O.; Widianto, E.D. Designing and Implementing the Arduino-Based Nutrition Feeding Automation System of a Prototype Scaled Nutrient Film Technique (NFT) Hydroponics Using Total Dissolved Solids (TDS) Sensor. In Proceedings of the 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, Indonesia, 18–19 October 2017; pp. 170–175. [Google Scholar]
  102. Kim, H.J.; Kim, W.K.; Roh, M.Y.; Kang, C.I.; Park, J.M.; Sudduth, K.A. Automated Sensing of Hydroponic Macronutrients Using a Computer-Controlled System with an Array of Ion-Selective Electrodes. Comput. Electron. Agric. 2013, 93, 46–54. [Google Scholar] [CrossRef]
  103. Erfianto, B.; Rakhmatsyah, A.; Ariyanto, E. Micro-Climate Control for Hydroponics in Greenhouses. In Proceedings of the 2020 8th International Conference on Information and Communication Technology (ICoICT), Yogyakarta, Indonesia, 24–26 June 2020; pp. 1–6. [Google Scholar]
  104. Siddiq, A.; Tariq, M.O.; Zehra, A.; Malik, S. ACHPA: A Sensor Based System for Automatic Environmental Control in Hydroponics. Food Sci. Technol. 2020, 40, 671–680. [Google Scholar] [CrossRef]
  105. Zhang, S.; Guo, Y.; Li, S.; Ke, Z.; Zhao, H.; Yang, J.; Wang, Y.; Li, D.; Wang, L.; Yang, W.; et al. Investigation on Environment Monitoring System for a Combination of Hydroponics and Aquaculture in Greenhouse. Inf. Process. Agric. 2022, 9, 123–134. [Google Scholar] [CrossRef]
  106. Ibayashi, H.; Kaneda, Y.; Imahara, J.; Oishi, N.; Kuroda, M.; Mineno, H. A Reliable Wireless Control System for Tomato Hydroponics. Sensors 2016, 16, 644. [Google Scholar] [CrossRef]
  107. Mishra, P.; Jimmy, L.; Ogunmola, G.A.; Phu, T.V.; Jayanthiladevi, A.; Latchoumi, T.P. Hydroponics Cultivation Using Real Time Iot Measurement System. J. Phys. Conf. Ser. 2020, 1712, 012040. [Google Scholar] [CrossRef]
  108. Mapari, R.G.; Bhangale, K.B.; Patil, P.; Tiwari, H.; Khot, S.; Rane, S. An IoT Based Automated Hydroponics Farming and Real Time Crop Monitoring. In Proceedings of the 2022 2nd International Conference on Intelligent Technologies (CONIT), Hubli, India, 24–26 June 2022; pp. 1–5. [Google Scholar] [CrossRef]
  109. Reyes-Yanes, A.; Martinez, P.; Ahmad, R. Real-Time Growth Rate and Fresh Weight Estimation for Little Gem Romaine Lettuce in Aquaponic Grow Beds. Comput. Electron. Agric. 2020, 179, 105827. [Google Scholar] [CrossRef]
  110. Yolanda, D.; Hindersah, H.; Hadiatna, F.; Triawan, M.A. Implementation of Real-Time Fuzzy Logic Control for NFT-Based Hydroponic System on Internet of Things Environment. In Proceedings of the 2016 6th International Conference on System Engineering and Technology (ICSET), Bandung, Indonesia, 3–4 October 2016; pp. 153–159. [Google Scholar]
  111. Saiz-Rubio, V.; Rovira-Más, F. From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy 2020, 10, 207. [Google Scholar] [CrossRef]
  112. Liu, J. Design and Implementation of an Intelligent Environmental-Control System: Perception, Network, and Application with Fused Data Collected from Multiple Sensors in a Greenhouse at Jiangsu, China. Int. J. Distrib. Sens. Netw. 2016, 12, 5056460. [Google Scholar] [CrossRef]
  113. Pitakphongmetha, J.; Boonnam, N.; Wongkoon, S.; Horanont, T.; Somkiadcharoen, D.; Prapakornpilai, J. Internet of Things for Planting in Smart Farm Hydroponics Style. In Proceedings of the 2016 International Computer Science and Engineering Conference (ICSEC), Chiang Mai, Thailand, 14–17 December 2016; pp. 1–5. [Google Scholar]
  114. Palande, V.; Zaheer, A.; George, K. Fully Automated Hydroponic System for Indoor Plant Growth. Procedia Comput. Sci. 2018, 129, 482–488. [Google Scholar] [CrossRef]
  115. Ferrández-Pastor, F.J.; García-Chamizo, J.M.; Nieto-Hidalgo, M.; Mora-Martínez, J. Precision Agriculture Design Method Using a Distributed Computing Architecture on Internet of Things Context. Sensors 2018, 18, 1731. [Google Scholar] [CrossRef] [PubMed]
  116. Kodali, R.K.; Jain, V.; Karagwal, S. IoT Based Smart Greenhouse. In Proceedings of the 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Agra, India, 21–23 December 2016; pp. 1–6. [Google Scholar]
  117. Jiao, J.; Ma, H.; Qiao, Y.; Du, Y.; Kong, W.; Wu, Z. Design of Farm Environmental Monitoring System Based on the Internet of Things. Adv. J. Food Sci. Technol. 2014, 6, 368–373. [Google Scholar] [CrossRef]
  118. Jayaraman, P.P.; Yavari, A.; Georgakopoulos, D.; Morshed, A.; Zaslavsky, A. Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt. Sensors 2016, 16, 1884. [Google Scholar] [CrossRef] [PubMed]
  119. Pooja, S.; Uday, D.V.; Nagesh, U.B.; Talekar, S.G. Application of MQTT Protocol for Real Time Weather Monitoring and Precision Farming. In Proceedings of the 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), Mysuru, India, 15–16 December 2017; pp. 1–6. [Google Scholar]
  120. Crisnapati, P.N.; Wardana, I.N.K.; Aryanto, I.K.A.A.; Hermawan, A. Hommons: Hydroponic Management and Monitoring System for an IOT Based NFT Farm Using Web Technology. In Proceedings of the 2017 5th International Conference on Cyber and IT Service Management (CITSM), Denpasar, Indonesia, 8–10 August 2017; pp. 1–6. [Google Scholar]
  121. Lamprinos, I.; Charalambides, M. Experimental Assessment of Zigbee as the Communication Technology of a Wireless Sensor Network for Greenhouse Monitoring. Int. J. Adv. Smart Sens. Netw. Syst. 2015, 5, 1–10. [Google Scholar] [CrossRef]
  122. Chieochan, O.; Saokaew, A.; Boonchieng, E. IOT for Smart Farm: A Case Study of the Lingzhi Mushroom Farm at Maejo University. In Proceedings of the 2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE), NakhonSiThammarat, Thailand, 12–14 July 2017; pp. 1–6. [Google Scholar]
  123. Akkaş, M.A.; Sokullu, R. An IoT-Based Greenhouse Monitoring System with Micaz Motes. Procedia Comput. Sci. 2017, 113, 603–608. [Google Scholar] [CrossRef]
  124. Abdul Halim, A.A.; Hassan, M.N.; Zakaria, A.; Kamarudin, L.; Abu Bakar, A. Internet of Things Technology for Greenhouse Monitoring and Management System Based on Wireless Sensor Network. ARPN J. Eng. Appl. Sci. 2016, 11, 13169–13175. [Google Scholar]
  125. Singh, T.A.; Chandra, J. IOT Based Green House Monitoring System. J. Comput. Sci. 2018, 14, 639–644. [Google Scholar] [CrossRef]
  126. Cambra, C.; Sendra, S.; Lloret, J.; Lacuesta, R. Smart System for Bicarbonate Control in Irrigation for Hydroponic Precision Farming. Sensors 2018, 18, 1333. [Google Scholar] [CrossRef] [PubMed]
  127. Yan, M.; Liu, P.; Zhao, R.; Liu, L.; Chen, W.; Yu, X.; Zhang, J. Field Microclimate Monitoring System Based on Wireless Sensor Network. J. Intell. Fuzzy Syst. 2018, 35, 1325–1337. [Google Scholar] [CrossRef]
  128. Yadav, S.; Kaushik, A.; Sharma, M.; Sharma, S. Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis. AgriEngineering 2022, 4, 424–460. [Google Scholar] [CrossRef]
  129. Terence, S.; Purushothaman, G. Systematic Review of Internet of Things in Smart Farming. Trans. Emerg. Telecommun. Technol. 2020, 31, e3958. [Google Scholar] [CrossRef]
  130. Liu, Z.; Xu, Q. Precision Irrigation Scheduling Using ECH2O Moisture Sensors for Lettuce Cultivated in a Soilless Substrate Culture. Water 2018, 10, 549. [Google Scholar] [CrossRef]
  131. Montesano, F.F.; Van Iersel, M.W.; Parente, A. Timer versus Moisture Sensor-Based Irrigation Control of Soilless Lettuce: Effects on Yield, Quality and Water Use Efficiency. Hortic. Sci. 2016, 43, 67–75. [Google Scholar] [CrossRef]
  132. Kang, S.; van Iersel, M.W.; Kim, J. Plant Root Growth Affects FDR Soil Moisture Sensor Calibration. Sci. Hortic. 2019, 252, 208–211. [Google Scholar] [CrossRef]
  133. Dutta, M.; Gupta, D.; Sahu, S.; Limkar, S.; Singh, P.; Mishra, A.; Kumar, M.; Mutlu, R. Evaluation of Growth Responses of Lettuce and Energy Efficiency of the Substrate and Smart Hydroponics Cropping System. Sensors 2023, 23, 1875. [Google Scholar] [CrossRef]
  134. Chen, C.; Zhang, X.; Xu, K.; Su, X.; Sun, J. Compensation Methods for PH Direct Measurement in Soilless Culture Substrates Using the All-Solid-Stated PH Sensor. IEEE Sens. J. 2021, 21, 26856–26867. [Google Scholar] [CrossRef]
  135. Yegül, U. Development of an Embedded Software and Control Kit to Be Used in Soilless Agriculture Production Systems. Sensors 2023, 23, 3706. [Google Scholar] [CrossRef] [PubMed]
  136. Shimpi, M.; Thorat, V.; Gavade, S.; Pawar, S.; Rajule, N. Smart Greenhouse Automation and Monitoring System. In Proceedings of the 2022 6th International Conference on Computing, Communication, Control and Automation (ICCUBEA), Pune, India, 26–27 August 2022; pp. 1–6. [Google Scholar]
  137. Lata, S.; Verma, H.K.; Roy, N.R.; Sagar, K. Development of Greenhouse-Application-Specific Wireless Sensor Node and Graphical User Interface. Int. J. Inf. Technol. 2023, 15, 211–218. [Google Scholar] [CrossRef]
  138. Hilal, Y.Y.; Khessro, M.K.; van Dam, J.; Mahdi, K. Automatic Water Control System and Environment Sensors in a Greenhouse. Water 2022, 14, 1166. [Google Scholar] [CrossRef]
  139. Vagisha; Rajesh, E.; Basheer, S.; Baskar, K. Hydroponics Soilless Smart Farming in Improving Productivity of Crop Using Intelligent Smart Systems. In Proceedings of the 2023 3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023, Uttar Pradesh, India, 22–24 February 2023; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2023. [Google Scholar]
  140. Dhanasekar, S.; Abarna, V.K.; Gayathri, V.; Valarmathi, G.; Madhumita, D.; Jeevitha, R. An Efficient Smart Agriculture System Based on The Internet of Things Using Aeroponics Method. In Proceedings of the 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 17–18 March 2023; Volume 1, pp. 1386–1391. [Google Scholar]
  141. Stevens, J.D.; Murray, D.; Diepeveen, D.; Toohey, D. Adaptalight: An Inexpensive PAR Sensor System for Daylight Harvesting in a Micro Indoor Smart Hydroponic System. Horticulturae 2022, 8, 105. [Google Scholar] [CrossRef]
  142. El Kasmi, N.; Lorhlam, B.; Fezghari, O.A.; Salih-Alj, Y. Hydroponic System in a Controlled Atmosphere and Substrate: Case Study in Morocco. In Proceedings of the 2022 IEEE International Conference on Mechatronics and Automation (ICMA), Guilin, China, 7–10 August 2022; pp. 274–279. [Google Scholar]
  143. Ullah, A.; Aktar, S.; Sutar, N.; Kabir, R.; Hossain, A. Cost Effective Smart Hydroponic Monitoring and Controlling System Using IoT. Intell. Control Autom. 2019, 10, 142–154. [Google Scholar] [CrossRef]
  144. Marques, G.; Aleixo, D.; Pitarma, R. Enhanced Hydroponic Agriculture Environmental Monitoring: An Internet of Things Approach. In Computational Science—ICCS 2019; Rodrigues João, M.F., Cardoso, P.J.S., Monteiro, J., Lam, R., Krzhizhanovskaya, V.V., Lees, M.H., Dongarra, J.J., Sloot, P.M.A., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 658–669. [Google Scholar]
  145. Bakhtar, N.; Chhabria, V.; Chougle, I.; Vidhrani, H.; Hande, R. IoT based Hydroponic Farm. In Proceedings of the 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 13–14 December 2018; pp. 205–209. [Google Scholar] [CrossRef]
  146. Ramakrishnam Raju, S.V.S.; Dappuri, B.; Ravi Kiran Varma, P.; Yachamaneni, M.; Verghese, D.M.G.; Mishra, M.K. Design and Implementation of Smart Hydroponics Farming Using IoT-Based AI Controller with Mobile Application System. J. Nanomater. 2022, 2022, 4435591. [Google Scholar] [CrossRef]
  147. Andrianto, H.; Suhardi; Faizal, A. Development of Smart Greenhouse System for Hydroponic Agriculture. In Proceedings of the 2020 International Conference on Information Technology Systems and Innovation (ICITSI), Bandung, Indonesia, 19–23 October 2020; pp. 335–340. [Google Scholar]
  148. Dhal, S.B.; Mahanta, S.; Gumero, J.; O’Sullivan, N.; Soetan, M.; Louis, J.; Gadepally, K.C.; Mahanta, S.; Lusher, J.; Kalafatis, S. An IoT-Based Data-Driven Real-Time Monitoring System for Control of Heavy Metals to Ensure Optimal Lettuce Growth in Hydroponic Set-Ups. Sensors 2023, 23, 451. [Google Scholar] [CrossRef]
  149. Mamatha, V.; Kavitha, J.C. Machine Learning Based Crop Growth Management in Greenhouse Environment Using Hydroponics Farming Techniques. Meas. Sens. 2023, 25, 100665. [Google Scholar] [CrossRef]
  150. Niswar, M. Design and Implementation of an Automated Indoor Hydroponic Farming System Based on the Internet of Things. Int. J. Comput. Digit. Syst. 2023, 14, 189–196. [Google Scholar]
  151. Alotaibi, H.; Karsou, W.; Khan, S.; Tohmeh, S.; Bashar, A. Bustani: A Microcontroller-Based Automated Hydroponic Vertical Farming Solution. In Proceedings of the 2023 IEEE International Conference on Agrosystem Engineering, Technology & Applications (AGRETA), Shah Alam, Malaysia, 9 September 2023; pp. 56–61. [Google Scholar]
  152. Venkatraman, M.; Surendran, R. Design and Implementation of Smart Hydroponics Farming for Growing Lettuce Plantation under Nutrient Film Technology. In Proceedings of the 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 4–6 May 2023; pp. 1514–1521. [Google Scholar]
  153. Danush, G.; Sri, R.; Karthikeyan, A. Smart Hydroponics System for Soilless Farming Based on Internet of Things. In Smart Technologies in Data Science and Communication; Ogudo Kingsley, A., Saha, S.K., Bhattacharyya, D., Eds.; Springer Nature: Singapore, 2023; pp. 271–280. [Google Scholar]
  154. Thilakarathne, N.N.; Abu Bakar, M.S.; Abas, P.E.; Yassin, H. Towards Making the Fields Talks: A Real-Time Cloud Enabled IoT Crop Management Platform for Smart Agriculture. Front. Plant Sci. 2023, 13, 1030168. [Google Scholar] [CrossRef]
  155. Albert, M.C.; Hans, H.; Karteja, H.; Widianto, M.H. Development of Hydroponic IoT-Based Monitoring System and Automatic Nutrition Control Using KNN. In Proceedings of the 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), Jakarta, Indonesia, 16 February 2023; pp. 974–979. [Google Scholar]
  156. Otieno, M. An Extensive Survey of Smart Agriculture Technologies: Current Security Posture. World J. Adv. Res. Rev. 2023, 18, 1207–1231. [Google Scholar] [CrossRef]
  157. Rettore de Araujo Zanella, A.; da Silva, E.; Pessoa Albini, L.C. Security Challenges to Smart Agriculture: Current State, Key Issues, and Future Directions. Array 2020, 8, 100048. [Google Scholar] [CrossRef]
  158. Demestichas, K.; Peppes, N.; Alexakis, T. Survey on Security Threats in Agricultural Iot and Smart Farming. Sensors 2020, 20, 6458. [Google Scholar] [CrossRef] [PubMed]
  159. Wang, P. On Defining Artificial Intelligence. J. Artif. Gen. Intell. 2019, 10, 1–37. [Google Scholar] [CrossRef]
  160. Soori, M.; Arezoo, B.; Dastres, R. Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics, a Review. Cogn. Robot. 2023, 3, 54–70. [Google Scholar] [CrossRef]
  161. Javaid, M.; Haleem, A.; Khan, I.H.; Suman, R. Understanding the Potential Applications of Artificial Intelligence in Agriculture Sector. Adv. Agrochem. 2023, 2, 15–30. [Google Scholar] [CrossRef]
  162. Chang, C.-L.; Chung, S.-C.; Fu, W.-L.; Huang, C.-C. Artificial Intelligence Approaches to Predict Growth, Harvest Day, and Quality of Lettuce (Lactuca Sativa L.) in a IoT-Enabled Greenhouse System. Biosyst. Eng. 2021, 212, 77–105. [Google Scholar] [CrossRef]
  163. Torres-Tello, J.; Ko, S.-B. Interpretability of Artificial Intelligence Models That Use Data Fusion to Predict Yield in Aeroponics. J. Ambient. Intell. Humaniz. Comput. 2023, 14, 3331–3342. [Google Scholar] [CrossRef]
  164. Wu, L.; Zhou, H.; Ma, X.; Fan, J.; Zhang, F. Daily Reference Evapotranspiration Prediction Based on Hybridized Extreme Learning Machine Model with Bio-Inspired Optimization Algorithms: Application in Contrasting Climates of China. J. Hydrol. 2019, 577, 123960. [Google Scholar] [CrossRef]
  165. Verma, M.S.; Gawade, S.D. A Machine Learning Approach for Prediction System and Analysis of Nutrients Uptake for Better Crop Growth in the Hydroponics System. In Proceedings of the 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India, 25–27 March 2021; pp. 150–156. [Google Scholar]
  166. Dhal, S.B.; Bagavathiannan, M.; Braga-Neto, U.; Kalafatis, S. Nutrient Optimization for Plant Growth in Aquaponic Irrigation Using Machine Learning for Small Training Datasets. Artif. Intell. Agric. 2022, 6, 68–76. [Google Scholar] [CrossRef]
  167. Rai, A.S.D.; Pawar, R.; Pandey, A.; Rajeshwari, C.S.; Gwal, A.K. Smart Artificial Intelligent-Based Controller for Hydroponic: New Technique for Soilless Plantation. In Machine Learning for Predictive Analysis; Joshi, A., Khosravy, M., Gupta, N., Eds.; Springer: Singapore, 2021; pp. 217–227. [Google Scholar]
  168. Ojo, M.O.; Zahid, A. Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects. Sensors 2022, 22, 7965. [Google Scholar] [CrossRef]
  169. Jaiswal, H.; Radha, K.; Singuluri, R.; Sampson, S.A. IoT and Machine Learning Based Approach for Fully Automated Greenhouse. In Proceedings of the 2019 IEEE Bombay Section Signature Conference (IBSSC), Mumbai, India, 26–28 July 2019; pp. 1–6. [Google Scholar]
  170. Chen, W.-H.; You, F. Semiclosed Greenhouse Climate Control Under Uncertainty via Machine Learning and Data-Driven Robust Model Predictive Control. IEEE Trans. Control Syst. Technol. 2022, 30, 1186–1197. [Google Scholar] [CrossRef]
  171. Fuentes-Peñailillo, F.; Carrasco, G.; Pérez, R.; Burgos, I.; Ewertz, F. Automating Seedling Counts in Horticulture Using Computer Vision and AI. Horticulturae 2023, 9, 1134. [Google Scholar] [CrossRef]
  172. Petropoulou, A.S.; van Marrewijk, B.; de Zwart, F.; Elings, A.; Bijlaard, M.; van Daalen, T.; Jansen, G.; Hemming, S. Lettuce Production in Intelligent Greenhouses—3D Imaging and Computer Vision for Plant Spacing Decisions. Sensors 2023, 23, 2929. [Google Scholar] [CrossRef]
  173. Khan, F.A.; Ibrahim, A.A.; M Zeki, A. Environmental Monitoring and Disease Detection of Plants in Smart Greenhouse Using Internet of Things. J. Phys. Commun. 2020, 4, 055008. [Google Scholar] [CrossRef]
  174. Fonteijn, H.; Afonso, M.; Lensink, D.; Mooij, M.; Faber, N.; Vroegop, A.; Polder, G.; Wehrens, R. Automatic Phenotyping of Tomatoes in Production Greenhouses Using Robotics and Computer Vision: From Theory to Practice. Agronomy 2021, 11, 1599. [Google Scholar] [CrossRef]
  175. Al Rasyid, M.U.H.; Sukaridhoto, S.; Iskandar, M.; Rifa’i, A. Integration of IoT and Chatbot for Aquaculture with Natural Language Processing. TELKOMNIKA Telecommun. Comput. Electron. Control 2020, 18, 640–648. [Google Scholar]
  176. Saad, M.H.M.; Hamdan, N.M.; Sarker, M.R. State of the Art of Urban Smart Vertical Farming Automation System: Advanced Topologies, Issues and Recommendations. Electronics 2021, 10, 1422. [Google Scholar] [CrossRef]
  177. Anitha, M.L.; Jnaneshwari, A.; Harshitha, S.R.; Bhanu Nagesha, M. IoT Based Automated Hydroponic System Using Light Intensity for Lettuce Growth. In Proceedings of the 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), Mandya, India, 26–27 December 2022; pp. 1–5. [Google Scholar]
  178. Farooqui, N.A.; Mishra, A.K.; Mehra, R. IOT Based Automated Greenhouse Using Machine Learning Approach. Int. J. Intell. Syst. Appl. Eng. 2022, 10, 226–231. [Google Scholar]
  179. Gao, J.; Zhang, F.; Zhang, J.; Yuan, T.; Yin, J.; Guo, H.; Yang, C. Development and Evaluation of a Pneumatic Finger-like End-Effector for Cherry Tomato Harvesting Robot in Greenhouse. Comput. Electron. Agric. 2022, 197, 106879. [Google Scholar] [CrossRef]
  180. Wang, B.; Ding, Y.; Wang, C.; Li, D.; Wang, H.; Bie, Z.; Huang, Y.; Xu, S. G-ROBOT: An Intelligent Greenhouse Seedling Height Inspection Robot. J. Robot. 2022, 2022, 9355234. [Google Scholar] [CrossRef]
  181. Wu, C.; Tang, X.; Xu, X. System Design, Analysis, and Control of an Intelligent Vehicle for Transportation in Greenhouse. Agriculture 2023, 13, 1020. [Google Scholar] [CrossRef]
  182. Su, L.; Liu, R.; Liu, K.; Li, K.; Liu, L.; Shi, Y. Greenhouse Tomato Picking Robot Chassis. Agriculture 2023, 13, 532. [Google Scholar] [CrossRef]
  183. Choi, T.; Park, J.; Kim, J.-J.; Shin, Y.-S.; Seo, H. Work Efficiency Analysis of Multiple Heterogeneous Robots for Harvesting Crops in Smart Greenhouses. Agronomy 2022, 12, 2844. [Google Scholar] [CrossRef]
  184. Long, Z.; Xiang, Y.; Lei, X.; Li, Y.; Hu, Z.; Dai, X. Integrated Indoor Positioning System of Greenhouse Robot Based on UWB/IMU/ODOM/LIDAR. Sensors 2022, 22, 4819. [Google Scholar] [CrossRef] [PubMed]
  185. 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]
  186. Kumar, A.; Singh, V.; Kumar, S.; Jaiswal, S.P.; Bhadoria, V.S. IoT Enabled System to Monitor and Control Greenhouse. Mater. Today Proc. 2022, 49, 3137–3141. [Google Scholar] [CrossRef]
  187. Ullah, I.; Fayaz, M.; Aman, M.; Kim, D. An Optimization Scheme for IoT Based Smart Greenhouse Climate Control with Efficient Energy Consumption. Computing 2022, 104, 433–457. [Google Scholar] [CrossRef]
  188. Benyezza, H.; Bouhedda, M.; Kara, R.; Rebouh, S. Smart Platform Based on IoT and WSN for Monitoring and Control of a Greenhouse in the Context of Precision Agriculture. Internet Things 2023, 23, 100830. [Google Scholar] [CrossRef]
  189. Maraveas, C. Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art. Appl. Sci. 2023, 13, 14. [Google Scholar] [CrossRef]
  190. Fernando, S.D.; Gamage, A.; De Silva, D.H. Machine Learning to Aid in the Process of Disease Detection and Management in Soilless Farming. In Proceedings of the 2022 IEEE 7th International conference for Convergence in Technology (I2CT), Mumbai, India, 7–9 April 2022; pp. 1–5. [Google Scholar]
  191. Fernando, S.; Nethmi, R.; Silva, A.; Perera, A.; De Silva, R.; Abeygunawardhana, P.W.K. AI Based Greenhouse Farming Support System with Robotic Monitoring. In Proceedings of the 2020 IEEE REGION 10 CONFERENCE (TENCON), Osaka, Japan, 16–19 November 2020; pp. 1368–1373. [Google Scholar]
  192. Talaviya, T.; Shah, D.; Patel, N.; Yagnik, H.; Shah, M. Implementation of Artificial Intelligence in Agriculture for Optimisation of Irrigation and Application of Pesticides and Herbicides. Artif. Intell. Agric. 2020, 4, 58–73. [Google Scholar] [CrossRef]
  193. Sammons, P.J.; Furukawa, T.; Bulgin, A. Autonomous Pesticide Spraying Robot for Use in a Greenhouse. In Proceedings of the Australian Conference on Robotics and Automation, Sydney, Australia, 5–7 December 2005; Volume 1, pp. 1–9. [Google Scholar]
  194. Wakchaure, M.; Patle, B.K.; Mahindrakar, A.K. Application of AI Techniques and Robotics in Agriculture: A Review. Artif. Intell. Life Sci. 2023, 3, 100057. [Google Scholar] [CrossRef]
  195. Garrity, J. Harnessing the Internet of Things for Global Development. SSRN Electron. J. 2015. [Google Scholar] [CrossRef]
  196. Kendall, H.; Clark, B.; Li, W.; Jin, S.; Jones, G.D.; Chen, J.; Taylor, J.; Li, Z.; Frewer, L.J. Precision Agriculture Technology Adoption: A Qualitative Study of Small-Scale Commercial “Family Farms” Located in the North China Plain. Precis. Agric. 2022, 23, 319–351. [Google Scholar] [CrossRef]
  197. Maloku, D. Adoption of Precision Farming Technologies: Usa and Eu Situation. SEA Pract. Appl. Sci. 2020, 8, 7–14. [Google Scholar]
  198. European Economic and Social Committee. Boosting the Use of Artificial Intelligence in Europe’s Micro, Small and Medium-Sized Enterprises; Publications Office of the European Union: Luxembourg, 2021. [Google Scholar] [CrossRef]
  199. Karnawat, M.; Trivedi, S.K.; Nagar, D.; Nagar, R. Future of AI in Agriculture. Biot. Res. Today 2020, 2, 927–929. [Google Scholar]
  200. Aytenfsu, S.A.; Beyene, A.M.; Getaneh, T.H. Controlling the Interior of Greenhouses Using Elman Recurrent Neural Network. In Proceedings of the 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), London, UK, 27–28 July 2020; pp. 32–39. [Google Scholar]
  201. Sankaranarayanan, S. Applications of Artificial Intelligence for Smart Agriculture. In AI-Based Services for Smart Cities and Urban. Infrastructure; IGI Global: Hershey, PA, USA, 2021; pp. 277–288. [Google Scholar]
  202. Panpatte, S.; Ganeshkumar, C. Artificial Intelligence in Agriculture Sector: Case Study of Blue River Technology. In Proceedings of the Second International Conference on Information Management and Machine Intelligence; Goyal, D., Gupta, A.K., Piuri, V., Ganzha, M., Paprzycki, M., Eds.; Springer Singapore: Singapore, 2021; pp. 147–153. [Google Scholar]
  203. Daoliang, L.; Chang, L. Recent Advances and Future Outlook for Artificial Intelligence in Aquaculture. Smart Agric. 2020, 2, 1–20. [Google Scholar] [CrossRef]
  204. Sharma, S.; Verma, K.; Hardaha, P. Implementation of Artificial Intelligence in Agriculture. J. Comput. Cogn. Eng. 2022, 2, 155–162. [Google Scholar] [CrossRef]
  205. Bu, F.; Wang, X. A Smart Agriculture IoT System Based on Deep Reinforcement Learning. Future Gener. Comput. Syst. 2019, 99, 500–507. [Google Scholar] [CrossRef]
  206. Hemming, S.; de Zwart, F.; Elings, A.; Righini, I.; Petropoulou, A. Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production. Sensors 2019, 19, 1807. [Google Scholar] [CrossRef]
  207. Wongchai, A.; Shukla, S.K.; Ahmed, M.A.; Sakthi, U.; Jagdish, M.; Kumar, R. Artificial Intelligence—Enabled Soft Sensor and Internet of Things for Sustainable Agriculture Using Ensemble Deep Learning Architecture. Comput. Electr. Eng. 2022, 102, 108128. [Google Scholar] [CrossRef]
  208. Sachithra, V.; Subhashini, L.D.C.S. How Artificial Intelligence Uses to Achieve the Agriculture Sustainability: Systematic Review. Artif. Intell. Agric. 2023, 8, 46–59. [Google Scholar] [CrossRef]
  209. Jain, A.; Patel, H.; Nagalapatti, L.; Gupta, N.; Mehta, S.; Guttula, S.; Mujumdar, S.; Afzal, S.; Sharma Mittal, R.; Munigala, V. Overview and Importance of Data Quality for Machine Learning Tasks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, New York, NY, USA, 6–10 July 2020; Association for Computing Machinery: New York, NY, USA, 2020; pp. 3561–3562. [Google Scholar]
  210. Budach, L.; Feuerpfeil, M.; Ihde, N.; Nathansen, A.; Noack, N.; Patzlaff, H.; Naumann, F.; Harmouch, H. The Effects of Data Quality on Machine Learning Performance. arXiv 2022, arXiv:2207.14529. [Google Scholar]
  211. Whang, S.E.; Roh, Y.; Song, H.; Lee, J.-G. Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective. VLDB J. 2023, 32, 791–813. [Google Scholar] [CrossRef]
  212. Whig, P. Leveraging AI for Sustainable Agriculture: Opportunities and Challenges. Trans. Latest Trends Artif. Intell. 2023, 4. [Google Scholar]
  213. Uddin, M.; Chowdhury, A.; Kabir, M.A. Legal and Ethical Aspects of Deploying Artificial Intelligence in Climate-Smart Agriculture. AI Soc. 2022, 1–14. [Google Scholar] [CrossRef]
  214. Dara, R.; Yang, Q.; Joy, R.; Flor, B.; Whitfield, S.; Mehdi, S.; Fard, H.; Kaur, J. Recommendations for Ethical and Responsible Use of Artificial Intelligence in Digital Agriculture. Front. Artif. Intell. 2022, 5. [Google Scholar] [CrossRef]
  215. Ryan, M. The Social and Ethical Impacts of Artificial Intelligence in Agriculture: Mapping the Agricultural AI Literature. AI Soc. 2022. [Google Scholar] [CrossRef]
  216. Rudrakar, S.; Rughani, P. IoT Based Agriculture (Ag-IoT): A Detailed Study on Architecture, Security and Forensics. Inf. Process. Agric. 2023. [Google Scholar] [CrossRef]
  217. Tzachor, A.; Devare, M.; King, B.; Avin, S.; Éigeartaigh, Ó.S. Responsible Artificial Intelligence in Agriculture Requires Systemic Understanding of Risks and Externalities. Nat. Mach. Intell. 2022, 4, 104–109. [Google Scholar] [CrossRef]
  218. Racovita, M. Industry Briefing: Cybersecurity for the Internet of Things and Artificial Intelligence in the AgriTech Sector; Industry Briefing PETRAS National Centre of Excellence for IoT Systems Cybersecurity: London, UK, 2021. [Google Scholar]
  219. Siregar, R.R.A.; Seminar, K.B.; Wahjuni, S.; Santosa, E. Vertical Farming Perspectives in Support of Precision Agriculture Using Artificial Intelligence: A Review. Computers 2022, 11, 135. [Google Scholar] [CrossRef]
  220. Bolandnazar, E.; Rohani, A.; Taki, M. Energy Consumption Forecasting in Agriculture by Artificial Intelligence and Mathematical Models. Energy Sources Part A Recovery Util. Environ. Eff. 2020, 42, 1618–1632. [Google Scholar] [CrossRef]
  221. Rodríguez, D.; Reca, J.; Martinez, J.; López-Luque, R.; Urrestarazu, M. Development of a New Control Algorithm for Automatic Irrigation Scheduling in Soilless Culture. Appl. Math. Inf. Sci. 2015, 9, 47–56. [Google Scholar] [CrossRef]
  222. Gayam, K.K.; Jain, A.; Gehlot, A.; Singh, R.; Akram, S.V.; Singh, A.; Anand, D.; Noya, I.D. Imperative Role of Automation and Wireless Technologies in Aquaponics Farming. Wirel. Commun. Mob. Comput. 2022, 2022, 8290255. [Google Scholar] [CrossRef]
  223. Tunalı, U.; Tüzel, I.H.; Tüzel, Y.; Şenol, Y. Estimation of Actual Crop Evapotranspiration Using Artificial Neural Networks in Tomato Grown in Closed Soilless Culture System. Agric. Water Manag. 2023, 284, 108331. [Google Scholar] [CrossRef]
  224. Moradi, A. Evaluation of the FAO Proposed Models for Reference Crop Evapotranspiration (ETo) Estimation in Hajiabad Watershed of Hormozgan Province. Watershed Manag. Res. J. 2023, 36, 121–133. [Google Scholar]
  225. Hua, D.; Hao, X.; Zhang, Y.; Qin, J. Uncertainty Assessment of Potential Evapotranspiration in Arid Areas, as Estimated by the Penman-Monteith Method. J. Arid. Land. 2020, 12, 166–180. [Google Scholar] [CrossRef]
  226. Ndulue, E.; Ranjan, R.S. Performance of the FAO Penman-Monteith Equation under Limiting Conditions and Fourteen Reference Evapotranspiration Models in Southern Manitoba. Theor. Appl. Clim. 2021, 143, 1285–1298. [Google Scholar] [CrossRef]
  227. Song, X.; Lu, F.; Xiao, W.; Zhu, K.; Zhou, Y.; Xie, Z. Performance of 12 Reference Evapotranspiration Estimation Methods Compared with the Penman–Monteith Method and the Potential Influences in Northeast China. Meteorol. Appl. 2019, 26, 83–96. [Google Scholar] [CrossRef]
  228. Paredes, P.; Pereira, L.S.; Almorox, J.; Darouich, H. Reference Grass Evapotranspiration with Reduced Data Sets: Parameterization of the FAO Penman-Monteith Temperature Approach and the Hargeaves-Samani Equation Using Local Climatic Variables. Agric. Water Manag. 2020, 240, 106210. [Google Scholar] [CrossRef]
  229. Djaman, K.; O’Neill, M.; Diop, L.; Bodian, A.; Allen, S.; Koudahe, K.; Lombard, K. Evaluation of the Penman-Monteith and Other 34 Reference Evapotranspiration Equations under Limited Data in a Semiarid Dry Climate. Theor. Appl. Clim. 2019, 137, 729–743. [Google Scholar] [CrossRef]
  230. Moratiel, R.; Bravo, R.; Saa, A.; Tarquis, A.M.; Almorox, J. Estimation of Evapotranspiration by the Food and Agricultural Organization of the United Nations (FAO) Penman–Monteith Temperature (PMT) and Hargreaves–Samani (HS) Models under Temporal and Spatial Criteria—A Case Study in Duero Basin (Spain). Nat. Hazards Earth Syst. Sci. 2020, 20, 859–875. [Google Scholar] [CrossRef]
  231. Cui, N.; He, Z.; Jiang, S.; Wang, M.; Yu, X.; Zhao, L.; Qiu, R.; Gong, D.; Wang, Y.; Feng, Y. Inter-Comparison of the Penman-Monteith Type Model in Modeling the Evapotranspiration and Its Components in an Orchard Plantation of Southwest China. Agric. Water Manag. 2023, 289, 108541. [Google Scholar] [CrossRef]
  232. Kim, H.J.; Chandrasekara, S.; Kwon, H.H.; Lima, C.; Kim, T. woong A Novel Multi-Scale Parameter Estimation Approach to the Hargreaves-Samani Equation for Estimation of Penman-Monteith Reference Evapotranspiration. Agric. Water Manag. 2023, 275, 108038. [Google Scholar] [CrossRef]
  233. Zheng, S.; Wang, T.; Wei, X. Estimating Grapevine Transpiration in Greenhouse with Three Different Methods in a Penman–Monteith Model in Northeast China. Irrig. Sci. 2022, 40, 13–27. [Google Scholar] [CrossRef]
  234. Kocian, A.; Carmassi, G.; Cela, F.; Chessa, S.; Milazzo, P.; Incrocci, L. IoT Based Dynamic Bayesian Prediction of Crop Evapotranspiration in Soilless Cultivations. Comput. Electron. Agric. 2023, 205, 107608. [Google Scholar] [CrossRef]
  235. Baille, M.; Baille, A.; Laury, J.C. A Simplified Model for Predicting Evapotranspiration Rate of Nine Ornamental Species vs. Climate Factors and Leaf Area. Sci. Hortic. 1994, 59, 217–232. [Google Scholar] [CrossRef]
  236. Popkova, E.G. Vertical Farms Based on Hydroponics, Deep Learning, and AI as Smart Innovation in Agriculture. In Smart Innovation in Agriculture; Popkova, E.G., Sergi, B.S., Eds.; Springer Nature: Singapore, 2022; pp. 257–262. ISBN 978-981-16-7633-8. [Google Scholar]
  237. Hassan, A.U.; Jibrin Musa, M.; Salihu, Y.O.; Oremeyi, A.A.; Ashafa, F. Artificial Intelligence Based Soilless Agriculture System Using Automatic Hydroponics Architecture. In Intelligent Systems and Machine Learning; Nandan Mohanty, S., Garcia Diaz, V., Satish Kumar, G.A.E., Eds.; Springer Nature: Cham, Switzerland, 2023; pp. 117–130. [Google Scholar]
  238. Patil, S.P.; Mathews, L.M.; Kumar, A.; Motgi, S.B.; Sinha, U. AI-Driven Hydroponic Systems for Lemon Basil. In Proceedings of the 2023 International Conference on Network, Multimedia and Information Technology (NMITCON), Bengaluru, India, 1–2 September 2023; pp. 1–6. [Google Scholar]
  239. Elvanidi, A.; Katsoulas, N.; Ferentinos, K.P.; Bartzanas, T.; Kittas, C. Hyperspectral Machine Vision as a Tool for Water Stress Severity Assessment in Soilless Tomato Crop. Biosyst. Eng. 2018, 165, 25–35. [Google Scholar] [CrossRef]
  240. Kanhekar, V.; Deshbhratar, T.; Matey, Y.; Kalbande, K.; Deshmukh, A. Hydroponic Farming Using IoT. In Proceedings of the 2022 International Conference on Edge Computing and Applications (ICECAA), Namakkal, India, 13–15 October 2022; pp. 583–586. [Google Scholar]
  241. Maraveas, C.; Piromalis, D.; Arvanitis, K.G.; Bartzanas, T.; Loukatos, D. Applications of IoT for Optimized Greenhouse Environment and Resources Management. Comput. Electron. Agric. 2022, 198, 106993. [Google Scholar] [CrossRef]
  242. Lakshmiprabha, K.E.; Govindaraju, C. Hydroponic-Based Smart Irrigation System Using Internet of Things. Int. J. Commun. Syst. 2023, 36, e4071. [Google Scholar] [CrossRef]
  243. Ang, H.N.; Lim, M.W.; Chua, W.S. Design of a Water Quality Monitoring System Utilizing IOT Platform for Hydroponics Application. AIP Conf. Proc. 2022, 2610, 040007. [Google Scholar] [CrossRef]
  244. Patil, A.S.; Tama, B.A.; Park, Y.; Rhee, K.-H. A Framework for Blockchain Based Secure Smart Green House Farming. In Advances in Computer Science and Ubiquitous Computing; Park, J.J., Loia, V., Yi, G., Sung, Y., Eds.; Springer: Singapore, 2018; pp. 1162–1167. [Google Scholar]
  245. Frikha, T.; Ktari, J.; Zalila, B.; Ghorbel, O.; Amor, N. Ben Integrating Blockchain and Deep Learning for Intelligent Greenhouse Control and Traceability. Alex. Eng. J. 2023, 79, 259–273. [Google Scholar] [CrossRef]
  246. Torky, M.; Hassanein, A.E. Integrating Blockchain and the Internet of Things in Precision Agriculture: Analysis, Opportunities, and Challenges. Comput. Electron. Agric. 2020, 178, 105476. [Google Scholar] [CrossRef]
  247. Bunge, A.C.; Wood, A.; Halloran, A.; Gordon, L.J. A Systematic Scoping Review of the Sustainability of Vertical Farming, Plant-Based Alternatives, Food Delivery Services and Blockchain in Food Systems. Nat. Food 2022, 3, 933–941. [Google Scholar] [CrossRef] [PubMed]
  248. Jamil, F.; Ibrahim, M.; Ullah, I.; Kim, S.; Kahng, H.K.; Kim, D.H. Optimal Smart Contract for Autonomous Greenhouse Environment Based on IoT Blockchain Network in Agriculture. Comput. Electron. Agric. 2022, 192, 106573. [Google Scholar] [CrossRef]
  249. Kobayashi, Y.; Kotilainen, T.; Carmona-García, G.; Leip, A.; Tuomisto, H.L. Vertical Farming: A Trade-off between Land Area Need for Crops and for Renewable Energy Production. J. Clean. Prod. 2022, 379, 134507. [Google Scholar] [CrossRef]
  250. 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]
  251. Bouadila, S.; Baddadi, S.; Ben Ali, R.; Ayed, R.; Skouri, S. Deploying Low-Carbon Energy Technologies in Soilless Vertical Agricultural Greenhouses in Tunisia. Therm. Sci. Eng. Prog. 2023, 42, 101896. [Google Scholar] [CrossRef]
  252. Arulvel, S.; Devadas, T.J.; Rufuss, D.D.W.; Prabakar, M.A. Renewable Energy Sources for Modern Agricultural Trends. In Cloud IoT Systems for Smart Agricultural Engineering; CRC Press: Boca Raton, FL, USA, 2022; pp. 223–242. [Google Scholar]
  253. Jiang, S.; Wang, S.; Yi, Z.; Zhang, M.; Lv, X. Autonomous Navigation System of Greenhouse Mobile Robot Based on 3D Lidar and 2D Lidar SLAM. Front. Plant Sci. 2022, 13. [Google Scholar] [CrossRef]
  254. 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]
  255. Jayasekara, C.; Banneka, S.; Pasindu, G.; Udawaththa, Y.; Wellalage, S.; Abeygunawardhane, P.K.W. Automated Crop Harvesting, Growth Monitoring and Disease Detection System for Vertical Farming Greenhouse. In Proceedings of the 2021 3rd International Conference on Advancements in Computing (ICAC), Colombo, Sri Lanka, 9–11 December 2021; pp. 228–233. [Google Scholar]
  256. Shrivastava, A.; Nayak, C.K.; Dilip, R.; Samal, S.R.; Rout, S.; Ashfaque, S.M. Automatic Robotic System Design and Development for Vertical Hydroponic Farming Using IoT and Big Data Analysis. Mater. Today Proc. 2023, 80, 3546–3553. [Google Scholar] [CrossRef]
  257. Walter, A.; Finger, R.; Huber, R.; Buchmann, N. Smart Farming Is Key to Developing Sustainable Agriculture. Proc. Natl. Acad. Sci. USA 2017, 114, 6148–6150. [Google Scholar] [CrossRef] [PubMed]
  258. Sørensen, C.A.G.; Kateris, D.; Bochtis, D. ICT Innovations and Smart Farming. In Information and Communication Technologies in Modern Agricultural Development; Salampasis, M., Bournaris, T., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 1–19. [Google Scholar]
  259. López, M.; Damsgaard, S.B.; Rodríguez, I.; Mogensen, P. Connecting Rural Areas: An Empirical Assessment of 5G Terrestrial-LEO Satellite Multi-Connectivity. In Proceedings of the 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), Florence, Italy, 20–23 June 2023; pp. 1–5. [Google Scholar]
  260. Roussaki, I.; Doolin, K.; Skarmeta, A.; Routis, G.; Lopez-Morales, J.A.; Claffey, E.; Mora, M.; Martinez, J.A. Building an Interoperable Space for Smart Agriculture. Digit. Commun. Netw. 2023, 9, 183–193. [Google Scholar] [CrossRef]
  261. Khatoon, P.S.; Ahmed, M. Importance of Semantic Interoperability in Smart Agriculture Systems. Trans. Emerg. Telecommun. Technol. 2022, 33, e4448. [Google Scholar] [CrossRef]
  262. Jawad, H.M.; Nordin, R.; Gharghan, S.K.; Jawad, A.M.; Ismail, M. Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. Sensors 2017, 17, 1781. [Google Scholar] [CrossRef] [PubMed]
  263. Meral, M.E.; Dinçer, F. A Review of the Factors Affecting Operation and Efficiency of Photovoltaic Based Electricity Generation Systems. Renew. Sustain. Energy Rev. 2011, 15, 2176–2184. [Google Scholar] [CrossRef]
  264. Okafor, N.U.; Alghorani, Y.; Delaney, D.T. Improving Data Quality of Low-Cost IoT Sensors in Environmental Monitoring Networks Using Data Fusion and Machine Learning Approach. ICT Express 2020, 6, 220–228. [Google Scholar] [CrossRef]
  265. Wiseman, L.; Sanderson, J.; Zhang, A.; Jakku, E. Farmers and Their Data: An Examination of Farmers’ Reluctance to Share Their Data through the Lens of the Laws Impacting Smart Farming. NJAS-Wagening. J. Life Sci. 2019, 90–91, 100301. [Google Scholar] [CrossRef]
  266. Yoon, C.; Lim, D.; Park, C. Factors Affecting Adoption of Smart Farms: The Case of Korea. Comput. Hum. Behav. 2020, 108, 106309. [Google Scholar] [CrossRef]
  267. Othman, Y.; Bataineh, K.; Al-Ajlouni, M.; Alsmairat, N.; Ayad, J.; Shiyab, S.; Al-Qarallah, B.; St Hilaire, R. Soilless Agriculture, Highlighting Their Advantages, Potential Drawbacks, and Key Areas of Impact. Soilless Culture: Management of Growing Substrate, Water, Nutrient, Salinity, Microorganism and Product Quality. Fresenius Env. Bull. 2019, 28, 3249–3260. [Google Scholar]
  268. Vega-Ibáñez, R.; Ortega-FarÃías, S.; Fuentes-Peñailillo, F.; Gutter, K.; Albornoz, J. Estimation of Midday Stem Water Potential in Grapevine Leaves (‘Cabernet Sauvignon’) Using Spectral Reflectance Indices. In Proceedings of the Acta Horticulturae International Society for Horticultural Science (ISHS), Leuven, Belgium, 6 February 2022; pp. 325–330. [Google Scholar]
  269. Gallardo, M.; Elia, A.; Thompson, R.B. Decision Support Systems and Models for Aiding Irrigation and Nutrient Management of Vegetable Crops. Agric. Water Manag. 2020, 240, 106209. [Google Scholar] [CrossRef]
  270. Kumari, S.; Pradhan, P.; Yadav, R.; Kumar, S. Hydroponic Techniques: A Soilless Cultivation in Agriculture. J. Pharmacogn. Phytochem. 2018, 7, 1886–1891. [Google Scholar]
  271. Cho, W.J.; Kim, H.J.; Jung, D.H.; Kim, D.W.; Ahn, T.I.; Son, J.E. On-Site Ion Monitoring System for Precision Hydroponic Nutrient Management. Comput. Electron. Agric. 2018, 146, 51–58. [Google Scholar] [CrossRef]
  272. Rouphael, Y.; Raimondi, G.; Caputo, R.; Pascale, S. De Fertigation Strategies for Improving Water Use Efficiency and Limiting Nutrient Loss in Soilless Hippeastrum Production. HortScience 2016, 51, 684–689. [Google Scholar] [CrossRef]
  273. Cámara-Zapata, J.M.; Brotons-Martínez, J.M.; Simón-Grao, S.; Martinez-Nicolás, J.J.; García-Sánchez, F. Cost–Benefit Analysis of Tomato in Soilless Culture Systems with Saline Water under Greenhouse Conditions. J. Sci. Food Agric. 2019, 99, 5842–5851. [Google Scholar] [CrossRef] [PubMed]
  274. Ghanayem, A.A.; Almohamed, S.; Al Assaf, A.; Majdalawi, M. Socioeconomic Analysis of Soil-Less Farming System-An Comparative Evidence from Jordan, The Middle East. Int. J. Food Agric. Econ. 2022, 10, 205–223. [Google Scholar] [CrossRef]
  275. Chen, D.; Zhao, H. Data Security and Privacy Protection Issues in Cloud Computing. In Proceedings of the 2012 International Conference on Computer Science and Electronics Engineering, Hangzhou, China, 23–25 March 2012; Volume 1, pp. 647–651. [Google Scholar]
  276. Wang, F.; Harindintwali, J.D.; Yuan, Z.; Wang, M.; Wang, F.; Li, S.; Yin, Z.; Huang, L.; Fu, Y.; Li, L.; et al. Technologies and Perspectives for Achieving Carbon Neutrality. Innovation 2021, 2, 100180. [Google Scholar] [CrossRef]
  277. Manos, D.-P.; Xydis, G. A Multi-Criteria Linear Model on Carbon Footprint in Vertical Farms and Its Relation to Energy Demand and Operational Costs. Environ. Sci. Pollut. Res. 2022, 29, 79331–79342. [Google Scholar] [CrossRef]
  278. Golzar, F.; Heeren, N.; Hellweg, S.; Roshandel, R. Optimisation of Energy-Efficient Greenhouses Based on an Integrated Energy Demand-Yield Production Model. Biosyst. Eng. 2021, 202, 1–15. [Google Scholar] [CrossRef]
  279. 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]
  280. 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]
  281. Muhammad, A.I.; Shitu, A.; Danhassan, U.A.; Kabir, M.H.; Tadda, M.A.; Lawal, A.M. Greenhouse Requirements for Soilless Crop Production: Challenges and Prospects for Plant Factories. In Next-Generation Greenhouses for Food Security; Shamshiri, R.R., Ed.; IntechOpen: Rijeka, Croatia, 2021; ISBN 978-1-83968-076-2. [Google Scholar]
  282. Karanisa, T.; Achour, Y.; Ouammi, A.; Sayadi, S. Smart Greenhouses as the Path towards Precision Agriculture in the Food-Energy and Water Nexus: Case Study of Qatar. Environ. Syst. Decis. 2022, 42, 521–546. [Google Scholar] [CrossRef]
  283. Formolli, M.; Kleiven, T.; Lobaccaro, G. Assessing Solar Energy Accessibility at High Latitudes: A Systematic Review of Urban Spatial Domains, Metrics, and Parameters. Renew. Sustain. Energy Rev. 2023, 177, 113231. [Google Scholar] [CrossRef]
  284. 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]
  285. Firdaus, N.; Samat, H.A.; Mohamad, N. Maintenance for Energy Efficiency: A Review. IOP Conf. Ser. Mater. Sci. Eng. 2019, 530. [Google Scholar] [CrossRef]
  286. Richa, A.; Touil, S.; Fizir, M.; Martinez, V. Recent Advances and Perspectives in the Treatment of Hydroponic Wastewater: A Review. Rev. Env. Sci. Biotechnol. 2020, 19, 945–966. [Google Scholar] [CrossRef]
  287. Putra, P.A.; Yuliando, H. Soilless Culture System to Support Water Use Efficiency and Product Quality: A Review. Agric. Agric. Sci. Procedia 2015, 3, 283–288. [Google Scholar] [CrossRef]
  288. Cucurachi, S.; Scherer, L.; Guinée, J.; Tukker, A. Life Cycle Assessment of Food Systems. One Earth 2019, 1, 292–297. [Google Scholar] [CrossRef]
  289. Toboso-Chavero, S.; Madrid-López, C.; Villalba, G.; Gabarrell Durany, X.; Hückstädt, A.B.; Finkbeiner, M.; Lehmann, A. Environmental and Social Life Cycle Assessment of Growing Media for Urban Rooftop Farming. Int. J. Life Cycle Assess. 2021, 26, 2085–2102. [Google Scholar] [CrossRef]
  290. Bonaguro, J.E.; Coletto, L.; Sambo, P.; Nicoletto, C.; Zanin, G. LCA Analysis of the Benefits Deriving from Sustainable Production Practices Applied to Cyclamen and Zonal Geranium. Preprints 2020. [Google Scholar] [CrossRef]
  291. Perrin, A.; Basset-Mens, C.; Gabrielle, B. Life Cycle Assessment of Vegetable Products: A Review Focusing on Cropping Systems Diversity and the Estimation of Field Emissions. Int. J. Life Cycle Assess. 2014, 19, 1247–1263. [Google Scholar] [CrossRef]
  292. Ilari, A.; Toscano, G.; Boakye-Yiadom, K.A.; Duca, D.; Foppa Pedretti, E. Life Cycle Assessment of Protected Strawberry Productions in Central Italy. Sustainability 2021, 13, 4879. [Google Scholar] [CrossRef]
  293. Maaoui, M.; Boukchina, R.; Hajjaji, N. Environmental Life Cycle Assessment of Mediterranean Tomato: Case Study of a Tunisian Soilless Geothermal Multi-Tunnel Greenhouse. Env. Dev. Sustain. 2021, 23, 1242–1263. [Google Scholar] [CrossRef]
  294. Smith, M.J. Getting Value from Artificial Intelligence in Agriculture. Anim. Prod. Sci. 2020, 60, 46–54. [Google Scholar] [CrossRef]
  295. Sharma, R. Artificial Intelligence in Agriculture: A Review. In Proceedings of the 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 6–8 May 2021; pp. 937–942. [Google Scholar]
  296. Eli-Chukwu, N.C. Applications of Artificial Intelligence in Agriculture: A Review. Eng. Technol. Appl. Sci. Res. 2019, 9, 4377–4383. [Google Scholar] [CrossRef]
  297. Sarkar, U.; Bannerjee, G.; Das, S.; Ghosh, I. Artificial Intelligence in Agriculture: A Literature Survey. Int. J. Sci. Res. Comput. Sci. Appl. Manag. Stud. 2018, 7. [Google Scholar]
  298. Klerkx, L.; Rose, D. Dealing with the Game-Changing Technologies of Agriculture 4.0: How Do We Manage Diversity and Responsibility in Food System Transition Pathways? Glob. Food Sec. 2020, 24, 100347. [Google Scholar] [CrossRef]
  299. Kos, D.; Kloppenburg, S. Digital Technologies, Hyper-Transparency and Smallholder Farmer Inclusion in Global Value Chains. Curr. Opin. Env. Sustain. 2019, 41, 56–63. [Google Scholar] [CrossRef]
  300. Regan, Á. ‘Smart Farming’ in Ireland: A Risk Perception Study with Key Governance Actors. NJAS-Wagening. J. Life Sci. 2019, 90–91, 100292. [Google Scholar] [CrossRef]
  301. DeLonge, M.S.; Robbins, T.; Basche, A.D.; Haynes-Maslow, L. The State of Sustainable Agriculture and Agroecology Research and Impacts: A Survey of U.S. Scientists. J. Agric. Food Syst. Community Dev. 2020, 9, 159–184. [Google Scholar] [CrossRef]
  302. König, B.; Janker, J.; Reinhardt, T.; Villarroel, M.; Junge, R. Analysis of Aquaponics as an Emerging Technological Innovation System. J. Clean. Prod. 2018, 180, 232–243. [Google Scholar] [CrossRef]
  303. Gardezi, M.; Joshi, B.; Rizzo, D.M.; Ryan, M.; Prutzer, E.; Brugler, S.; Dadkhah, A. Artificial Intelligence in Farming: Challenges and Opportunities for Building Trust. Agron. J. 2023, 1–12. [Google Scholar] [CrossRef]
  304. Goh, H.-H.; Vinuesa, R. Regulating Artificial-intelligence Applications to achieve the Sustainable Development Goals. Discov. Sustain. 2021, 2, 52. [Google Scholar] [CrossRef]
  305. Fields, J.S.; Owen, J.; Lamm, A.; Altland, J.E.; Jackson, B.E.; Zheng, Y.; Oki, L.; Fontenot, K.; Samtani, J.; Campbell, B. Soilless Substrate Science: A North American Needs Assessment to Steer Soilless Substrate Research into the Future. In Proceedings of the Acta Horticulturae, Leuven, Belgium, 20 August 2021; International Society for Horticultural Science (ISHS): Leuven, Belgium, 2021; pp. 313–318. [Google Scholar]
  306. Türkten, H.; Ceyhan, V. Environmental Efficiency in Greenhouse Tomato Production Using Soilless Farming Technology. J. Clean. Prod. 2023, 398, 136482. [Google Scholar] [CrossRef]
  307. Qian, C.; Murphy, S.I.; Orsi, R.H.; Wiedmann, M. How Can AI Help Improve Food Safety? Annu. Rev. Food Sci. Technol. 2023, 14, 517–538. [Google Scholar] [CrossRef]
  308. Sun, W.; Zhang, Z.; Chen, Y.; Luan, F. Heterogeneous Effects of Robots on Employment in Agriculture, Industry, and Services Sectors. Technol. Soc. 2023, 75, 102371. [Google Scholar] [CrossRef]
  309. Mizik, T. How Can Precision Farming Work on a Small Scale? A Systematic Literature Review. Precis. Agric. 2023, 24, 384–406. [Google Scholar] [CrossRef]
  310. Marinello, F.; Zou, X.; Liu, Z.; Zhu, X.; Zhang, W.; Qian, Y.; Li, Y.; Karunathilake, E.M.B.M.; Le, A.T.; Heo, S.; et al. The Path to Smart Farming: Innovations and Opportunities in Precision. Agriculture 2023, 13, 1593. [Google Scholar] [CrossRef]
  311. Barnes, A.P.; Soto, I.; Eory, V.; Beck, B.; Balafoutis, A.; Sánchez, B.; Vangeyte, J.; Fountas, S.; van der Wal, T.; Gómez-Barbero, M. Exploring the Adoption of Precision Agricultural Technologies: A Cross Regional Study of EU Farmers. Land Use Policy 2019, 80, 163–174. [Google Scholar] [CrossRef]
  312. Ugur, M.; Mitra, A. Technology Adoption and Employment in Less Developed Countries: A Mixed-Method Systematic Review. World Dev. 2017, 96, 1–18. [Google Scholar] [CrossRef]
  313. Medici, M.; Pedersen, S.M.; Carli, G.; Tagliaventi, M.R. Environmental Benefits of Precision Agriculture Adoption. Econ. Agro-Aliment. 2020, 21, 637–656. [Google Scholar] [CrossRef]
  314. Banerjee, A.; Paul, K.; Varshney, A.; Nandru, R.; Badhwar, R.; Sapre, A.; Dasgupta, S. Chapter 8—Soilless Indoor Smart Agriculture as an Emerging Enabler Technology for Food and Nutrition Security amidst Climate Change. In Plant Nutrition and Food Security in the Era of Climate Change; Kumar, V., Srivastava, A.K., Suprasanna, P., Eds.; Academic Press: Cambridge, MA, USA, 2022; pp. 179–225. ISBN 978-0-12-822916-3. [Google Scholar]
  315. Mitchell, S.; Weersink, A.; Bannon, N. Adoption Barriers for Precision Agriculture Technologies in Canadian Crop Production. Can. J. Plant Sci. 2021, 101, 412–416. [Google Scholar] [CrossRef]
  316. Lazo, R.P.; Gonzabay, J.Q. Economic Analysis of Hydroponic Lettuce under Floating Root System in Semi-Arid Climate. Granja 2020, 31, 121–133. [Google Scholar] [CrossRef]
  317. D’Ostuni, M.; Zaffi, L.; Appolloni, E.; Orsini, F. Understanding the Complexities of Building-Integrated Agriculture. Can Food Shape the Future Built Environment? Futures 2022, 144, 103061. [Google Scholar] [CrossRef]
  318. Setiowati, S.; Wardhani, R.N.; Riandini; Siregar, E.B.A.; Saputra, R.; Sabrina, R.A. Fertigation Control System on Smart Aeroponics Using Sugeno’s Fuzzy Logic Method. In Proceedings of the 2022 8th International Conference on Science and Technology (ICST), Montreal, QC, Canada, 2–4 August 2022; Volume 1, pp. 1–6. [Google Scholar]
  319. Garzón, J.; Montes, L.; Garzón, J.; Lampropoulos, G. Systematic Review of Technology in Aeroponics: Introducing the Technology Adoption and Integration in Sustainable Agriculture Model. Agronomy 2023, 13, 2517. [Google Scholar] [CrossRef]
  320. van Os, E.A.; Gieling, T.H.; Lieth, J.H. Chapter 13—Technical Equipment in Soilless Production Systems. In Soilless Culture: Theory and Practice; Raviv, M., Lieth, J.H., Bar-Tal, A., Eds.; Elsevier: Boston, MA, USA, 2019; pp. 587–635. ISBN 978-0-444-63696-6. [Google Scholar]
  321. Di Gioia, F.; Rosskopf, E.N. Organic Hydroponics: A US Reality Challenging the Traditional Concept of “Organic” and “Soilless” Cultivation. In Proceedings of the Acta Horticulturae International Society for Horticultural Science (ISHS), Leuven, Belgium, 9 September 2021; pp. 275–282. [Google Scholar]
  322. Fruscella, L.; Kotzen, B.; Milliken, S. Organic Aquaponics in the European Union: Towards Sustainable Farming Practices in the Framework of the New EU Regulation. Rev. Aquac. 2021, 13, 1661–1682. [Google Scholar] [CrossRef]
  323. Wang, L.; Iddio, E. Energy Performance Evaluation and Modeling for an Indoor Farming Facility. Sustain. Energy Technol. Assess. 2022, 52, 102240. [Google Scholar] [CrossRef]
  324. Hedau, N.K.; Tuti, M.D.; Stanley, J.; Mina, B.L.; Agrawal, P.K.; Bisht, J.K.; Bhatt, J.C. Energy-Use Efficiency and Economic Analysis of Vegetable Cropping Sequences under Greenhouse Condition. Energy Effic. 2014, 7, 507–515. [Google Scholar] [CrossRef]
  325. Weidner, T.; Yang, A.; Hamm, M.W. Energy Optimisation of Plant Factories and Greenhouses for Different Climatic Conditions. Energy Convers. Manag. 2021, 243, 114336. [Google Scholar] [CrossRef]
  326. Zhang, M.; Yan, T.; Wang, W.; Jia, X.; Wang, J.; Klemeš, J.J. Energy-Saving Design and Control Strategy towards Modern Sustainable Greenhouse: A Review. Renew. Sustain. Energy Rev. 2022, 164, 112602. [Google Scholar] [CrossRef]
  327. Sambo, P.; Nicoletto, C.; Giro, A.; Pii, Y.; Valentinuzzi, F.; Mimmo, T.; Lugli, P.; Orzes, G.; Mazzetto, F.; Astolfi, S.; et al. Hydroponic Solutions for Soilless Production Systems: Issues and Opportunities in a Smart Agriculture Perspective. Front. Plant Sci. 2019, 10, 923. [Google Scholar] [CrossRef] [PubMed]
  328. Savvas, D.; Giannothanasis, E.; Ntanasi, T.; Karavidas, I.; Drakatos, S.; Panagiotakis, I.; Neocleous, D.; Ntatsi, G. Improvement and Validation of a Decision Support System to Maintain Optimal Nutrient Levels in Crops Grown in Closed-Loop Soilless Systems. Agric. Water Manag. 2023, 285, 108373. [Google Scholar] [CrossRef]
  329. Langenfeld, N.J.; Pinto, D.F.; Faust, J.E.; Heins, R.; Bugbee, B. Principles of Nutrient and Water Management for Indoor Agriculture. Sustainability 2022, 14, 10204. [Google Scholar] [CrossRef]
  330. Ares, G.; Chheang, S.L.; Jaeger, S.R. Buying Vertically Farmed Produce: Comparison of People with Lower and Higher Stated Purchase Likelihood. J. Sens. Stud. 2023, 38, e12832. [Google Scholar] [CrossRef]
  331. Broad, G.M.; Marschall, W.; Ezzeddine, M. Perceptions of High-Tech Controlled Environment Agriculture among Local Food Consumers: Using Interviews to Explore Sense-Making and Connections to Good Food. Agric. Hum. Values 2022, 39, 417–433. [Google Scholar] [CrossRef]
  332. Caputo, S. History, Techniques and Technologies of Soil-Less Cultivation. In Small Scale Soil-less Urban Agriculture in Europe; Caputo, S., Ed.; Springer International Publishing: Cham, Switzerland, 2022; pp. 45–86. ISBN 978-3-030-99962-9. [Google Scholar]
  333. Knight, J.; Weir, S.; Woldehanna, T. The Role of Education in Facilitating Risk-Taking and Innovation in Agriculture. J. Dev. Stud. 2003, 39, 1–22. [Google Scholar] [CrossRef]
  334. Takahashi, K.; Muraoka, R.; Otsuka, K. Technology Adoption, Impact, and Extension in Developing Countries’ Agriculture: A Review of the Recent Literature. Agric. Econ. 2020, 51, 31–45. [Google Scholar] [CrossRef]
  335. Giri, S.; Daw, T.M.; Hazra, S.; Troell, M.; Samanta, S.; Basu, O.; Marcinko, C.L.J.; Chanda, A. Economic Incentives Drive the Conversion of Agriculture to Aquaculture in the Indian Sundarbans: Livelihood and Environmental Implications of Different Aquaculture Types. Ambio 2022, 51, 1963–1977. [Google Scholar] [CrossRef]
  336. Goodman, W.; Minner, J. Will the Urban Agricultural Revolution Be Vertical and Soilless? A Case Study of Controlled Environment Agriculture in New York City. Land Use Policy 2019, 83, 160–173. [Google Scholar] [CrossRef]
  337. Lanre Tajudeen, A.; Solomon Taiwo, O. Soilless Farming—A Key Player in the Realisation of “Zero Hunger” of the Sustainable Development Goals in Nigeria. Int. J. Ecol. Sci. Environ. Eng. 2018, 5, 1–7. [Google Scholar]
  338. Randolph, O.; Asiabanpour, B. Automation of an Off-Grid Vertical Farming System to Optimize Power Consumption. In Advances in Parallel & Distributed Processing, and Applications; Arabnia, H.R., Deligiannidis, L., Grimaila, M.R., Hodson, D.D., Joe, K., Sekijima, M., Tinetti, F.G., Eds.; Springer International Publishing: Cham, Switzerland, 2021; pp. 1015–1022. [Google Scholar]
  339. Muller, A.; Ferré, M.; Engel, S.; Gattinger, A.; Holzkämper, A.; Huber, R.; Müller, M.; Six, J. Can Soil-Less Crop Production Be a Sustainable Option for Soil Conservation and Future Agriculture? Land Use Policy 2017, 69, 102–105. [Google Scholar] [CrossRef]
Table 1. Summary of recent technological advancements in hydroponics, highlighting their main advantages and disadvantages.
Table 1. Summary of recent technological advancements in hydroponics, highlighting their main advantages and disadvantages.
Advanced Hydroponic TechnologyMain AdvantagesMain DisadvantagesReferences
AI-Based Monitoring
High precision in nutrient and pH
detection, yield optimization
High cost, technical skills required for operation[41,43,44]
Precision Agriculture TechniquesEfficient resource use, improved crop qualityHigh initial investment, complexity in implementation[45,46]
Advanced Moisture and Nutrient SensorsReal-time monitoring, improved
irrigation efficiency
Installation and maintenance cost, potential technical failures[47]
Automated Climate
Control Systems
Precise environmental control, improved crop quality and yieldHigh energy consumption,
operational costs
Full-Spectrum LED
Energy efficiency, improved plant growthHigh initial cost, potential for plant stress if not managed correctly[49,50]
Mobile Apps for Crop ManagementRemote access for monitoring and
control, ease of use
Connectivity dependency, feature limitations depending on the app[51]
Table 2. Overview of Substrate Materials in Soilless Agriculture, Highlighting Their Main Advantages and Disadvantages.
Table 2. Overview of Substrate Materials in Soilless Agriculture, Highlighting Their Main Advantages and Disadvantages.
Substrate MaterialsMain AdvantagesMain DisadvantagesReferences
CoirRenewable, excellent water
retention, good aeration
Potential for high salt content, inconsistent quality[72,73]
PerliteLightweight, good drainage, sterileExpensive, non-renewable, can float and cause uneven water distribution[74]
RockwoolExcellent water retention, sterile, easy to useNon-biodegradable, manufacturing
process has environmental impacts
VermiculiteHigh water retention,
good nutrient-holding capacity
Expensive, non-renewable, potential for compaction over time[76]
Expanded Clay PebblesReusable, good drainage,
High initial cost, potential for algae growth[77]
BiocharRenewable, improves soil structure, high nutrient retentionVariable quality, potential for high pH
Rice HullsRenewable, biodegradable,
good aeration
Potential for pest issues, decomposes over time[73,74]
Table 3. Overview of Real-Time Monitoring Systems for Optimizing Crop Growth and Resource Utilization, Highlighting Their Advantages and Disadvantages.
Table 3. Overview of Real-Time Monitoring Systems for Optimizing Crop Growth and Resource Utilization, Highlighting Their Advantages and Disadvantages.
Real-Time Monitoring SystemsAdvantagesDisadvantagesReferences
Soil Moisture SensorsEfficient water use prevents
Initial setup cost, maintenance[130,131,132]
Nutrient SensorsOptimizes nutrient delivery,
reduces waste
High cost, calibration required[80]
pH SensorsMaintains optimal pH levels,
improves nutrient absorption
Calibration needed, potential for
Temperature SensorsOptimizes climate control,
improves yield
Energy consumption, cost[133,135,136,137,138,139]
Light SensorsEfficient light use, improves
Initial cost, limited to certain crops[137,140,141]
Humidity SensorsPrevents mold, optimizes water useCalibration required, maintenance[133,137,138]
CO2 SensorsOptimizes plant growth,
improves yield
High cost, complexity, high
maintenance cost
Table 4. Overview of the Integration of Artificial Intelligence (AI) in Soilless Crop Systems, Highlighting Their Advantages, Disadvantages, and Key Use-Cases.
Table 4. Overview of the Integration of Artificial Intelligence (AI) in Soilless Crop Systems, Highlighting Their Advantages, Disadvantages, and Key Use-Cases.
AI Applications in Soilless SystemsAdvantagesDisadvantagesKey Use-CasesReferences
Predictive AnalyticsOptimizes yield, reduces wasteHigh setup cost, data quality issuesYield prediction,
disease detection,
evapotranspiration rate prediction
Machine Learning
Adaptive, improves over timeComplexity, requires expertiseNutrient management,
climate control
Computer VisionReal-time monitoring,
high accuracy
Hardware cost, limited to certain cropsDisease detection, growth monitoring[171,172,173,174]
Natural Language
Processing (NLP)
User-friendly interfaces,
easy monitoring
Limited capabilities, language barriersUser interaction, data
Robotics and AutomationLabor-saving,
high-energy efficiency
High initial investment,
Harvesting, planting,
IoT IntegrationCentralized control,
real-time data, energy use efficiency
Security risks,
connectivity issues
Sensor data aggregation, remote control[185,186,187,188]
Table 6. Overview of Potential Limitations and Hurdles to Widespread Adoption of Soilless Agriculture, Highlighting Their Impact, Possible Solutions, and Areas Affected.
Table 6. Overview of Potential Limitations and Hurdles to Widespread Adoption of Soilless Agriculture, Highlighting Their Impact, Possible Solutions, and Areas Affected.
Limitations and BarriersImpact on AdoptionPossible SolutionsAreas AffectedReferences
High Initial CostBarrier to entry for
small-scale farmers
Government subsidies,
financing options
Infrastructure, technology[273,274,316,317]
Steep learning curve,
specialized skills required
Training programs,
user-friendly technology
System management, data analysis[317,318,319,320]
Compliance risks, lack of standardizationDevelopment of industry standards, regulatory
Food safety, environmental impact[303,321,322]
Sustainability concerns,
operational costs
Renewable energy sources,
energy-efficient systems
Climate control, lighting[323,324,325,326]
Water QualityRisk of contamination,
nutrient imbalances
Water treatment systems, real-time monitoringNutrient delivery, plant health[243,327,328,329]
Consumer skepticism, market adoptionPublic awareness campaigns,
transparent labeling
Market penetration,
consumer trust
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Fuentes-Peñailillo, F.; Gutter, K.; Vega, R.; Silva, G.C. New Generation Sustainable Technologies for Soilless Vegetable Production. Horticulturae 2024, 10, 49.

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Fuentes-Peñailillo F, Gutter K, Vega R, Silva GC. New Generation Sustainable Technologies for Soilless Vegetable Production. Horticulturae. 2024; 10(1):49.

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Fuentes-Peñailillo, Fernando, Karen Gutter, Ricardo Vega, and Gilda Carrasco Silva. 2024. "New Generation Sustainable Technologies for Soilless Vegetable Production" Horticulturae 10, no. 1: 49.

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