Aug 2026· Scientific Journal of Informatics· 0 citations· 57 references
TL;DR
The review shows that AI-IoT technologies are primarily applied in crop disease detection, precision agriculture, environmental monitoring, yield prediction, and livestock health monitoring, and the five-layered framework introduced here provides the first inductively derived organising structure that explicitly connects AI-IoT infrastructure to SDG-aligned food security outcomes.
Abstract
Purpose: Achieving global food security while maintaining environmentally sustainable agricultural systems remains a critical challenge amid population growth, climate variability, and resource constraints. Artificial Intelligence (AI) and the Internet of Things (IoT) have emerged as transformative technologies that support data-driven agricultural practices. This study systematically examines the applications, opportunities, challenges, and adoption factors of AI-IoT integration in smart agriculture, with particular emphasis on its potential contributions to food security and sustainable farming.
Methods: A systematic literature review (SLR) was conducted following the PRISMA 2020 guidelines. Publications were retrieved from Scopus, IEEE Xplore, and ScienceDirect covering the period 2020-2024. From an initial 431 records, 17 empirical studies met the inclusion criteria and were analysed using narrative and thematic synthesis.
Result: The review shows that AI-IoT technologies are primarily applied in crop disease detection, precision agriculture, environmental monitoring, yield prediction, and livestock health monitoring. These technologies enable real-time decision support, early disease detection, productivity improvement, and resource optimisation. However, challenges remain, including data limitations, infrastructure constraints, integration complexity, and high deployment costs.
Novelty: The study proposes a layered smart agriculture framework linking technological infrastructure, application domains, adoption conditions, operational outcomes, and sustainability impacts. The findings highlight key factors necessary for successful implementation, including infrastructure readiness, affordability, technological reliability, and capacity development for farmers. Crucially, the review demonstrates that the field of AI-IoT smart agriculture is technically advanced but socio-technically incomplete: the evidence base is dominated by proof-of-concept studies from Asia, with no empirical representation from Africa or the Americas, creating what this study terms an AI-IoT agricultural equity gap that fundamentally limits the technology’s contribution to global food security. The five-layered framework introduced here provides the first inductively derived organising structure that explicitly connects AI-IoT infrastructure to SDG-aligned food security outcomes, offering a replicable analytical scaffold for future empirical and policy research in this domain.
It is argued that AI-ML integration improves productivity in agriculture in terms of crop yield prediction, disease prediction and optimization of resources, amongst others, and a comprehensive strategy for future work in designing sustainable agrifood systems is proposed.
Anita Veerappa Karkikatti, R. H. Goudar, Vijayalaxmi N. Rathod et al.· Discover Artificial Intellig...· 0 citations
Abstract: Background The agriculture sector is now encountering many unique problems due to climate change, resource exhaustion, food demand, and environmental damage. Traditional methods of agriculture involve homogeneous field operations, which result in inefficient utilization of water, fertilizers, and pesticides. The application of innovative technology like the Internet of Things (IoT), Geographic Information System (GIS), Remote Sensing (RS), and Artificial Intelligence (AI) has revolutionized the concept of precision agriculture. Objective: The purpose of this research is to examine the incorporation of IoT, GIS, Remote Sensing, and AI in precision environmental agriculture and assess the possible contribution these technologies could make towards enhancing agricultural productivity and sustainability. Methods A comprehensive literature-based analytical study was conducted by reviewing recent scientific publications from 2020–2025. Information regarding IoT sensors, GIS-based spatial analysis, satellite and UAV remote sensing, and AI-driven predictive models was synthesized to develop an integrated precision agriculture framework. Comparative analysis was performed to assess the contribution of each technology toward sustainable food production. Results and Conclusion IoT, GIS, Remote Sensing, and Artificial Intelligence together provide a complete digital ecosystem for precision agriculture. The use of these technologies ensures environmental monitoring, crop health analysis, disease prediction, optimum irrigation scheduling, and estimation of yield in advance. When used together, these technologies help in reducing wastage, increasing productivity, and minimizing environmental impact. This research paper ends with a conclusion stating that digital agriculture is one of the main strategies of the future towards sustainable food production systems.
Muhammad Azeem, Fozia Bibi, Tahira Nisa et al.· Pakistan Journal of Positive...· 0 citations
The agro-food industry is quickly digitizing to solve resource restrictions, climate unpredictability, and sustainable food production using Internet of Things (IoT), Artificial intelligence (AI), cloud computing, and data analytics. Due to rigid irrigation schedules and limited field monitoring, traditional agriculture wastes water, reduces crop yield, and harms the environment. The IoT-Driven Precision Agriculture (IoT-PA) system in this paper optimizes agricultural resource use using distributed sensor networks, real-time soil moisture and weather monitoring, and intelligent irrigation control. The proposed platform gathers environmental data, analyses field conditions, and automatically recommends irrigation to maximize crop growth and avoid water waste. Farmers may make data-driven agricultural decisions with continuous monitoring and fast notifications from the framework. The IoT-PA architecture improves irrigation efficiency, agricultural yield, and sustainable resource management compared to traditional farming. Higher agricultural production and efficient water and resource use assist Sustainable Development Goal (SDG) 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production) of the United Nations Sustainable Development Goals. The suggested smart agricultural system is scalable and practicable, promoting sustainable food production and environmental conservation.
T. G. Sakthivel, R. Ashok, A. M et al.· Journal of Visualized Experi...· 0 citations
The field of agriculture is encountering considerable difficulties in fulfilling the rising stresses in order to produce food while maintaining sustainability and efficient source utilization. Numerous technologies currently employed in agricultural practices for monitoring crop growth, soil efficiency, and nutrient levels have faced challenges. Some of these technologies have proven inadequate due to variations in frequency and distance range within smart farming applications. To tackle these issues, this review highlights an integration of biosensors, bioelectronics and Internet of Things (IoT) technology into agricultural performs, which has become a hopeful answer. The paper discusses how these sensing elements are integrated with IoT architectures, microcontroller-based nodes, wireless sensor networks, and cloud-enabled analytics to support precision irrigation, nutrient management, disease indication, and yield-oriented crop supervision. In addition, it compares major IoT communication technologies, reviews current applications and limitations, identifies research gaps in field deployment and system integration, and outlines future directions for robust, scalable, and intelligent agricultural biosensing systems.
Climate change poses significant challenges to agricultural systems worldwide, particularly in developing regions where farming livelihoods are highly vulnerable to climatic shocks and environmental stresses. Digital innovations have emerged as critical enablers of Climate-Smart Agriculture (CSA), offering new opportunities to enhance climate adaptation, resilience, and sustainable agricultural transformation. This study conducted a scoping review to systematically map and synthesize existing evidence on the role of digital technologies in supporting climate adaptation and resilience in agriculture. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) framework, literature was sourced from major databases, including Scopus, Web of Science, ScienceDirect, AGRIS, IEEE Xplore, and Google Scholar. Eighteen studies met the eligibility criteria and were included in the review. The findings identified eight major categories of digital innovations: Artificial Intelligence (AI) and Machine Learning (ML), Internet of Things (IoT), Remote Sensing and Geographic Information Systems (GIS), Drones and Unmanned Aerial Vehicles (UAVs), Mobile-Based Advisory Services, Blockchain Technology, Big Data Analytics, and Integrated Digital Decision-Support Systems. These technologies contribute to climate adaptation and resilience through improved climate information services, precision agriculture, resource-use efficiency, climate-risk forecasting, environmental monitoring, extension service delivery, and resilient agricultural value chains. The review further revealed that institutional capacity, socioeconomic conditions, technological infrastructure, and policy frameworks significantly influence technology adoption and effectiveness. The study concludes that digital innovations are transformative tools for enhancing agricultural productivity, climate resilience, and food security. Strengthening digital infrastructure, policy support, financing mechanisms, and capacity-building initiatives will be essential for scaling inclusive and sustainable digital climate-smart agriculture, particularly in climate-vulnerable regions such as Sub-Saharan Africa.
Adetomiwa Kolapo, M. A. Otitoju· Frontiers in Agronomy· 1 citation
This paper systematically reviews the emerging field of smart urban agriculture, defined as the integration of information and communication technologies (ICTs) into food production practices within and around built-up urban areas. Although urban agriculture and digital agriculture have each generated substantial scholarship, their intersection remains conceptually fragmented and empirically uneven. Following the PRISMA 2020 guidelines, this study reviews 143 English-language articles indexed in Web of Science and Scopus between 2016 and 2026, combining bibliometric mapping with structured thematic coding. The analysis shows that smart urban agriculture has expanded rapidly since 2021, but remains geographically concentrated and disciplinarily dispersed. Current research is organized mainly around IoT and sensor networks, machine learning, soilless cultivation, vertical farming, plant factories, and controlled-environment agriculture. ICT applications are most mature in enclosed, data-rich, and technically controllable systems, where they support monitoring, prediction, automation, and resource optimization. By contrast, community-based, open-space, and governance-oriented forms of urban agriculture remain underexplored. By systematically linking ICT families with different urban agriculture production settings, this review clarifies the field’s emerging knowledge structure and demonstrates that technological development remains uneven across agricultural forms and socio-institutional contexts.
Ruhang Wei, Dan Wu, Wulijiang Mulati et al.· Agriculture· 0 citations
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