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Internet of Things-Based Smart Precision Farming in Soilless Agriculture: Opportunities and Challenges for Global Food Security

Monica Dutta, Deepali Gupta, Sumegh Tharewal, Deepam Goyal, Jasminder Kaur Sandhu, Manjit Kaur, Ahmad Ali Alzubi, Jazem Mutared Alanazi

arXiv:2503.13528v3eess.SPcs.LG

TL;DR

Food security is threatened by population growth, shrinking cultivable land, and climate change. The paper combines a review of IoT-driven soilless farming with bibliometric and comparative analyses, concluding that controlled, automated systems can improve resource efficiency and support scalable, year-round cultivation while facing investment and energy challenges.

  • Problem

    Population growth, declining cultivable land, and climate change create a need for food production that uses limited land, water, and other resources efficiently.

  • Method

    The paper reviews IoT-based soilless farming, evaluates controlled-environment and vertical-farming approaches, and applies bibliometric and comparative analyses to identify trends, gaps, and future directions.

  • Results

    IoT-enabled hydroponic, aeroponic, and aquaponic systems automate environmental control, improve resource efficiency, and support high-yield crop production throughout the year.

  • Takeaways & Limitations

    IoT-driven soilless agriculture offers a pathway for urban, vertical, and controlled-environment farming, with interdisciplinary research, policy support, and technological advances needed for global scaling.

Abstract

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The rapid growth of the global population and the continuous decline in cultivable land pose significant threats to food security. This challenge worsens as climate change further reduces the availability of farmland. Soilless agriculture, such as hydroponics, aeroponics, and aquaponics, offers a sustainable solution by enabling efficient crop cultivation in controlled environments. The integration of the Internet of Things (IoT) with smart precision farming improves resource efficiency, automates environmental control, and ensures stable and high-yield crop production. IoT-enabled smart farming systems utilize real-time monitoring, data-driven decision-making, and automation to optimize water and nutrient usage while minimizing human intervention. This paper explores the opportunities and challenges of IoT-based soilless farming, highlighting its role in sustainable agriculture, urban farming, and global food security. These advanced farming methods ensure greater productivity, resource conservation, and year-round cultivation. However, they also face challenges such as high initial investment, technological dependency, and energy consumption. Through a comprehensive study, bibliometric analysis, and comparative analysis, this research highlights current trends and research gaps. It also outlines future directions for researchers, policymakers, and industry stakeholders to drive innovation and scalability in IoT-driven soilless agriculture. By emphasizing the benefits of vertical farming and Controlled Environment Agriculture (CEA)-enabled soilless techniques, this paper supports informed decision-making to address food security challenges and promote sustainable agricultural innovations.

I. INTRODUCTION

Population growth, urbanization, shrinking farmland, and climate pressures intensify food-security challenges. The paper presents IoT-enabled soilless farming as a resource-efficient approach while reviewing its benefits, evidence, constraints, and research directions.

  • Motivation: Global population growth, urban expansion, farmland loss, and climate change increase pressure to produce more food with fewer resources.The paper links these pressures to food-supply imbalance, resource depletion, pollution, and food-security concerns.
  • Motivation: Soilless cultivation supports urban agriculture by providing an affordable, sustainable, and efficient alternative to traditional farming.Hydroponic, aeroponic, and related systems reduce dependence on soil and can support cultivation in controlled environments.
  • IoT-enabled farming: IoT-based smart precision farming uses sensors, automation, and environmental control to improve resource efficiency and support year-round crop production.The paper emphasizes optimized water and nutrient use, reduced human intervention, and improved agricultural yields.
  • Benefits and evidence: Soilless systems can improve crop performance through shorter vegetation periods, efficient water use, and controlled growing conditions.Reported studies describe enhanced growth, yield, or biomass across lettuce, cress, basil, eggplant, potato, lettuce, pak choy, and spinach.
  • Challenges: The reviewed evidence also identifies constraints including high installation costs, substantial energy consumption, limited crop suitability, and variable fertilizer performance.Soilless cultivation mainly supports herbaceous plants, while some alternatives, such as food-waste fertilizer for cucumber, reduced growth relative to commercial fertilizer.

A. CLASSIFICATION OF SOILLESS CULTIVATION

Soilless cultivation comprises aeroponics, hydroponics, and aquaponics, with IoT systems adding sensor-based monitoring, automated control, and data-driven optimization. Reported applications include resource savings, faster production, predictive accuracy, and improved crop or fish productivity.

  • Classification: Soilless cultivation is classified into aeroponics, hydroponics, and aquaponics, using air, water, or an aquaculture-integrated hydroponic ecosystem.Aeroponics grows plants in air, hydroponics uses nutrient-rich water, and aquaponics couples hydroponic cultivation with aquaculture.
  • Aeroponics: Aeroponic systems expose roots to nutrient mist and use sensors, actuators, and cloud processing to regulate environmental conditions and nutrient delivery.These systems can monitor temperature, humidity, and nutrient levels while automatically adjusting mist spraying and nutrient distribution.
  • Aeroponics: 80% savings in water and energy, doubled productivity per unit area, and a reduction from 75 to 45 days were reported for an Egyptian smart greenhouse.The greenhouse integrated hydroponics and aeroponics and automatically adjusted environmental conditions by plant type and season.
  • Hydroponics: Hydroponic systems monitor water level, electrical conductivity, pH, and nutrient availability for real-time control of growing conditions.An IoT-based Batavia lettuce system reported 80% lower water usage and 50% shorter cultivation time.
  • Hydroponics: Hydroponic applications reported 93% growth-rate prediction accuracy, while an NFT system using coconut coir provided the best results in one greenhouse study.Indoor vertical systems were also designed to operate independently of external environmental conditions and support food production in limited space.
  • Aquaponics: Aquaponics recirculates nutrients between fish culture and hydroponic plants, while IoT and machine-learning systems reported 99.13% and 99% monitoring accuracy.A separate sensor-based Tilapia system reported a 6% increase in fish growth with consistent circulation and minimal power consumption.

B. MERITS AND DEMERITS OF SOILLESS

Soilless techniques support high yields, efficient resource use, and year-round cultivation, but their comparative performance depends on method-specific trade-offs. Hydroponics is versatile and scalable, aquaponics emphasizes sustainability, and aeroponics maximizes space efficiency while requiring complex infrastructure.

  • Shared merits and demerits: Soilless systems provide high yields, efficient water use, and year-round cultivation, but require high initial investment and technological dependency.These trade-offs are summarized through comparative and SWOT analyses of aeroponics, hydroponics, and aquaponics.
  • Method-specific trade-offs: Aeroponics is the most space-efficient method, whereas hydroponics is versatile and scalable and aquaponics is less efficient but more sustainable.The comparison considers yield, water and nutrient use, energy consumption, space efficiency, scalability, and environmental impact.
  • Method-specific trade-offs: Aeroponics and aquaponics require higher energy consumption because of their complex setups.The comparison identifies energy demand as a distinguishing disadvantage alongside the methods’ resource-efficiency benefits.

C. COMPONENTS USED IN SOILLESS TECHNIQUES

Soilless systems combine external substrates, nutrient solutions, and monitoring controls to support plant growth while improving resource management. The section covers substrate choices, nutrient composition, environmental impacts, and key measurements such as EC, irrigation timing, WUE, and yield.

  • System components: Soilless cultivation uses external substrates to support plants and separate nutrient solutions to provide required inputs.Substrates may be organic or inorganic, while nutrient solutions supply essential elements for growth.
  • Cultivation substrates: Organic substrates include compost, peat, and bark, whereas inorganic options include rockwool and perlite.Substrate selection considers physical, chemical, biological, cost, sustainability, and crop-compatibility factors.
  • Nutrient solutions: Nutrient solutions provide macronutrients and micronutrients, including nitrogen, phosphorus, potassium, boron, zinc, copper, iron, calcium, and magnesium.Hydroponic systems enable precise monitoring of nutrient requirements and solution characteristics such as pH, TDS, and EC.
  • Resource efficiency: Soilless cultivation can improve water efficiency and productivity compared with soil-based cultivation.Reported comparisons found traditional cultivation consumed multiple times more water, while hydroponic plants transpired less water and showed qualitative productivity advantages.
  • Environmental considerations: Environmental impacts from PVC, PP, and substrates remain concerns, although recycling and sustainable practices can reduce these effects.The section identifies materials used in pipes, grow tubes, reservoirs, and cultivation media as sources of environmental impact.
  • Key measurements: Monitoring and control focus on nutrient-solution EC, irrigation duration, water-use efficiency, and crop yield.The section defines WUE as crop biomass produced per unit of water consumed and describes sensor-based control for resource-efficient production.

III. SMART PRECISION FARMING

Smart precision farming applies data analytics, machine learning, IoT, sensors, and automation to soilless cultivation. These technologies support precise input delivery, automated operations, and prediction of yields and cultivation expenses.

  • Technology integration: Data analytics, ML, and IoT have improved soilless cultivation by supporting smarter and more self-sustaining farming systems.The paper links these technologies with modern agricultural automation and monitoring.
  • Precision input management: Smart precision farming applies fertilizers and nutrients frequently and in controlled amounts according to crop requirements.This approach is described as improving nutrient and resource efficiency while supporting plant growth.
  • Automated cultivation: A sensor- and microcontroller-based hydroponic system collected environmental data in the cloud and automated irrigation through scheduling.The system was implemented for brinjal cultivation under controlled environment agriculture.
  • Automation: Actuators can automate farming tasks using fuzzy logic, ML algorithms, and deep neural networks, reducing dependence on manual labor.ML and DL techniques were also proposed for predicting yield and cultivation expenses.
  • Evolution: The evolution toward smart precision farming represents a shift from traditional substrate-based cultivation to automated, data-driven techniques.Figure 5 is identified as illustrating this transition.

A. EVOLUTION AND APPLICATIONS OF SMART

Smart precision farming evolved through automation, IoT, intelligent sensing, and data-driven agriculture. Its applications include continuous monitoring, precise input administration, climate control, irrigation automation, and AI-enabled prediction.

  • Evolution: Advances in automation, AI, IoT, intelligent sensors, sensor networks, and communication protocols drove the transition to smart precision farming.The paper also identifies UAV applications in irrigation, fertilization, spraying, weed management, monitoring, disease detection, and phenotyping.
  • Applications: Real-time monitoring enables data-driven administration of fertilizers, pesticides, nutrients, and water.The paper presents continuous monitoring as a central feature of smart farming systems.
  • Applications: AI, ML, and DL support monitoring and yield prediction based on real-time environmental conditions.These technologies are described as extensions of smart precision farming capabilities.
  • Applications: Applications include continuous soil-moisture monitoring, climate control, and automated irrigation.These applications are depicted in Figure 6.

B. SENSORS USED IN SMART PRECISION FARMING

IoT-enabled smart precision farming uses sensors, communication, cloud systems, and actuators to monitor and regulate growing conditions. CEA and vertical farming extend these capabilities through controlled environments, while costs, energy demand, and technological dependence remain constraints.

  • Sensors: Sensors continuously collect environmental and agronomic data to support automated decisions and efficient resource use.The paper identifies pH, water level, temperature, humidity, EC, and hyperspectral sensing as relevant functions.
  • Controlled Environment Agriculture: CEA externally supplies nutrients and environmental factors to reduce dependence on natural resources and climatic conditions.The approach analyzes and modifies agronomic variables to meet plant requirements, supporting faster growth and higher yields.
  • Automated control: CEA systems can transmit sensor data to the cloud, alert users when parameters deviate, and use actuators to restore optimal conditions.This monitoring-and-control sequence was described for lettuce cultivation.
  • CEA classification: CEA supports soil-based and soilless systems, including aeroponics, hydroponics, and aquaponics.Hydroponics are further classified into open and closed systems, while aquaponics may be coupled or decoupled.
  • Benefits: CEA and smart precision farming can conserve water, optimize nutrients, reduce fertilizer use, and minimize environmental impact.The paper associates these systems with year-round, pesticide-free, and higher-quality production.
  • Challenges: CEA-enabled vertical farming faces high initial costs, expensive materials, limited skilled labor, high energy consumption, and technological dependence.Renewable energy is identified as a possible way to mitigate energy-related concerns.

V. INTEGRATION AND APPLICATION OF TECHNOLOGIES

IoT, AI, ML, and DL support real-time monitoring, automation, and data-driven decision-making in CEA-enabled soilless farming. These technologies improve resource management, crop monitoring, disease detection, productivity, and production efficiency.

  • Soilless applications: Hydroponics, aeroponics, and aquaponics use integrated technologies to optimize resource use, reduce cultivation time, and increase crop yields.The technologies are presented as enhancing CEA-enabled soilless farming methods.
  • Machine learning: Machine learning analyzes input data, identifies patterns, predicts outcomes, and optimizes performance, helping reduce production costs while improving crop yield and quality.ML also supports analysis of nonlinear relationships between dependent and independent variables.
  • Applications: Applications include crop disease classification, greenhouse monitoring, crop-demand prediction, and agricultural risk mitigation.Examples span sensor-based monitoring of CO2, moisture, humidity, light, and temperature; crop pre-planning; and AI-based decision support.
  • Technology integration: IoT, AI, ML, and DL enable automated, data-driven decisions that improve monitoring, resource management, and environmental responsiveness.These technologies support real-time information collection, pattern analysis, prediction, and automated control.
  • Agricultural outcomes: AI-powered precision farming improves crop monitoring, disease detection, and resource management while minimizing waste, environmental impact, and production costs.The cited synthesis associates these improvements with year-round, faster, and higher-quality crop yields.

VI. BIBLIOMETRIC ANALYSIS

The study combines bibliometric analysis, systematic literature review, and thematic analysis to examine research on soilless cultivation, CEA, and smart precision farming. It uses a filtered Scopus dataset analyzed with RStudio and Biblioshiny to identify trends, contributors, and collaboration patterns.

  • Analytical approach: Bibliometric analysis uses statistical techniques and graphical representations to evaluate publication impact and identify research trends, authors, sources, institutions, and collaborations.The method supports decisions by researchers, publishers, institutions, and policymakers.
  • Analytical approach: The study combines bibliometric analysis, systematic literature review, and thematic analysis to triangulate quantitative trends with qualitative research themes.The three approaches are presented as complementary perspectives on the literature.
  • Study scope: The research conducts a bibliometric analysis of literature on soilless cultivation, CEA, and smart precision farming.This defines the study’s central bibliometric scope.
  • Data source: Scopus was selected for its multidisciplinary peer-reviewed coverage, bibliometric indicators, collaboration networks, and validated metadata.The database provides h-index values, citation counts, and collaboration information.
  • Data collection: The search used aeroponics, aquaponics, hydroponics, soil cultivation, and controlled environment agriculture as keywords.The query was applied to titles, abstracts, and keywords.
  • Data processing: The dataset was restricted to English-language articles published from 2013 to 2023, reducing 8,214 retrieved records to 7,764.The filtered records were downloaded as a CSV file and analyzed in RStudio with Biblioshiny.

B. SYNTHESIS, ANALYSIS, AND FINDINGS

The bibliometric findings show rapid growth in research on soilless and soil-based cultivation, with identifiable publication, citation, source, author, and collaboration patterns. China, leading authors, and major journals occupy prominent positions in the mapped research landscape.

  • Analysis outputs: The Scopus dataset was analyzed with Biblioshiny to generate publication trends, co-citation networks, source rankings, author analyses, and keyword visualizations.These outputs include annual production, relevant sources, co-occurrence networks, and word clouds.
  • Publication trends: 7,764 articles were retrieved, and annual publications rose from 371 in 2013 to 1,064 in 2022.A notable increase occurred from 748 articles in 2019 to 1,053 in 2021.
  • Publication trends: 14% of all articles were published individually in 2021, 2022, and 2023, while output was approximately 5% in 2013 and 2014.The publication trend is described as exponential growth in the field.
  • Citation patterns: 636 citations made Singh Sekhon B’s 2014 article the most cited document, followed by Lin KH’s 2012 article with 547 citations.Figure 11 represents global citations against listed documents.
  • Sources: Acta Horticulturae was the most productive source with 209 articles by 2023, followed by Environmental Science and Pollution Research with 199.Ecotoxicology and Environmental Safety ranked third with 170 articles and received the highest number of citations among the listed sources.
  • Authors: Wang Y was the most prolific author with 135 publications, while Ali S had the highest total citations at 3,262.Li Y, Wang Y, and Zhang J shared the highest h-index of 29.
  • Collaboration networks: The author collaboration network contains seven clusters, with the densest collaboration reported between Li C and Li Y in the red cluster.Strong collaboration was also reported between Wang Y and Wang Q in the blue cluster.

G. COUNTRIES’ NETWORK ANALYSIS

The country analysis maps research output and international collaboration in soilless and soil cultivation using CEA. China leads publication volume, while many major contributors show more domestic than international production.

  • Country analysis: The country analysis examines corresponding authors, national scientific production, and international collaboration networks.These dimensions are represented through country rankings, production maps, and collaboration links.
  • International collaboration: Many leading countries have lower multiple-country production than single-country production, indicating that research is conducted mainly domestically.The analysis states that expanded international partnerships could enhance research impact and knowledge exchange.
  • Map interpretation: Country maps encode higher publication output with darker blue, lower contributions with lighter blue, and no output with grey.The maps provide a visual comparison of research production across countries.
  • Scientific production: China leads research output with 10,651 publications, followed by the USA with 2,947 and India with 2,829.Brazil, Italy, Japan, Germany, Indonesia, Spain, and Pakistan are also identified as major contributors.
  • International collaboration: Australia–Austria is the most frequent collaboration, followed by Australia’s partnerships with Denmark and Canada.Argentina is identified as the most collaborative country, partnering with numerous countries.

H. INFLUENTIAL ANALYSIS

The influential-analysis section maps keyword, institutional, thematic, and author-keyword patterns in smart soilless agriculture research. Hydroponics dominates the field’s keyword landscape, while institutional and thematic networks show broad international development.

  • Keyword co-occurrence network: Hydroponics forms the largest keyword co-occurrence cluster, with metabolism and plant root leading the second- and third-largest clusters.
  • Institutions collaboration network: The institution collaboration network contains five clusters, with Zhejiang University leading the largest blue cluster.
  • Thematic evolution: Thematic evolution is divided into three periods: 2013–2016, 2017–2020, and 2021–2023.
  • Word cloud: Hydroponics is the most frequent author keyword, followed by aquaponics, controlled environment agriculture, IoT, and lettuce.

VII. DISCUSSIONS

The discussion presents IoT-enabled soilless farming as a response to food-security pressures, combining automated cultivation with bibliometric evidence about the field. It emphasizes productivity and resource-efficiency opportunities alongside adoption barriers and future technical priorities.

  • Research landscape: 7,764 articles were analyzed bibliometrically, with research concentrated in agricultural and biological sciences and major contributions from China and Zhejiang University.The dataset included 7,764 Scopus articles; China contributed 10,651 research articles from 2013–2023 according to the reported national analysis.
  • Opportunities: IoT-enabled hydroponics, aeroponics, and aquaponics automate environmental control, improve resource efficiency, and reduce human intervention in soilless farming.
  • Challenges: High initial investment, energy consumption, technological dependency, and specialized expertise constrain large-scale adoption.
  • Opportunities: Smart soilless technologies support consistent, high-yield crop production throughout the year while optimizing resources and minimizing waste.
  • Research implications: The study identifies global trends, leading contributors, emerging research gaps, and the importance of interdisciplinary research and policy support for scaling smart farming.
  • Future directions: Future research targets low-power sensors, renewable energy, nutrient recycling, AI crop monitoring, and edge or fog computing.
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