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.
Abstract
Precision agriculture is increasingly leveraging advancements in artificial intelligence (AI) and machine learning (ML) to address global food demands sustainably. Previous literature on smart agriculture may have considered AI, IoT, and 5G but has not integrated the technology synergies or may overlook the newer trends, including XAI and edge-ready lightweight AI models. This paper addresses those gaps by synthesizing new trends, socio-economic challenges, and relevant policy frameworks that shape the future of AI-enabled agricultural practices. A structured literature review methodology was adopted using Scopus, IEEE Xplore, ScienceDirect, and SpringerLink databases. A total of 78 high-quality studies published between 2018 and 2025 were systematically analyzed.In particular, this review paper synthesizes insights from literature to argue that AI-ML integration improves productivity in agriculture in terms of crop yield prediction, disease prediction and optimization of resources, amongst others. However, there are several adoption barriers such as data privacy issues, costly infrastructural setup, and lack of digital literacy skills among small-scale farmers. We emphasize the importance of using edge computing for faster decision-making and robotics in scaling up precision solutions and also highlight the use of XAI to foster trust via interpretability in AI. Finally, we propose a comprehensive strategy for future work in designing sustainable agrifood systems based on: (1) interoperable systems using standards and protocols, (2) affordable and cyber-resilient systems, and (3) policy initiatives to democratize AI technologies. By addressing technical, ethical, and scalability challenges, this work advocates for a balanced convergence of human expertise and automated systems, ensuring equitable progress toward sustainability goals.
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.
B. Ndlovu, Kudakwashe Maguraushe· Scientific Journal of Inform...· 0 citations
Agriculture is undergoing a major transformation driven by artificial intelligence (AI), shifting traditional farming toward a more data-driven and technology-enabled system. This review explores the role of AI in modern agriculture, focusing on its key components, applications, benefits, limitations and future prospects. AI technologies such as machine learning, deep learning, computer vision, natural language processing, robotics, predictive analytics, IoT and cloud-edge computing are increasingly integrated into agricultural systems to enhance decision-making, productivity and sustainability. These technologies enable precision farming, crop monitoring, disease detection, forecasting, automation and supply chain optimization, improving efficiency while reducing resource wastage. However, AI adoption remains limited due to high costs, lack of technical skills, data and infrastructure gaps, regulatory uncertainties and concerns about privacy and adaptability. Additional challenges include farmer resistance and the potential loss of traditional agricultural knowledge, highlighting the need for a balanced integration of technology and human expertise. Looking ahead, advances in autonomous systems, multimodal data integration and adaptive learning are expected to make agriculture more resilient, inclusive and sustainable. Overall, AI offers strong potential to improve productivity and strengthen global food security under changing environmental conditions. Continued investment in research, digital infrastructure and capacity building will be essential to realize the full potential of AI for sustainable agricultural development.
D. Sharma· International Journal of Far...· 0 citations
Artificial Intelligence of Things (AIoT), which refers to the combination of Artificial Intelligence (AI) and the Internet of Things (IoT), has become an important research area in smart farming. Although the literature on AIoT has expanded rapidly, little is known about its overall development, intellectual structure, and emerging research directions. To bridge this research gap, this paper examines the AIoT literature in smart farming using a bibliometric approach based on publications indexed in the Web of Science Core Collection. Bibliometric techniques, including performance analysis, keyword co-occurrence analysis, and bibliographic coupling, were employed to examine publication trends, most relevant journals in the field, top scholars, countries, institutions, and research themes. The findings show a rapid growth of AIoT research, driven mainly by engineering and computer science disciplines. Five major research themes are identified, covering AI applications in agriculture, IoT-enabled smart farming, intelligent sensing systems, agricultural data management, and prediction models. Based on these findings, the study discusses the evolution of AIoT research, identifies current research gaps, and proposes a future research agenda focusing on artificial intelligence, interoperability, cybersecurity, data quality, technology adoption, sustainability, and responsible innovation. This study provides an extensive overview of AIoT research in smart farming and provides useful directions for researchers and practitioners interested in the future development of digital agriculture.
Artificial intelligence (AI) is increasingly applied in agriculture to support data-driven decision-making, improve productivity, and enhance resource management. Small-scale farmers, who produce a significant share of the world’s food yet often operate under resource constraints, may particularly benefit from these technologies. However, it remains unclear how AI research addresses the needs of small-scale farming systems and the extent to which farmers directly interact with AI tools. This study conducts a systematic literature review to examine the applications, impacts, and challenges of AI in small-scale agriculture. The review followed the PRISMA 2020 guidelines and applied a structured review methodology, using the Web of Science, Scopus, and EBSCOhost databases. A total of 182 studies were identified and analyzed. The results show a rapid increase in publications after 2020, with research concentrated mainly in Africa and Asia. Most studies focus on technical AI applications such as plant disease detection, crop yield prediction, crop classification, and environmental monitoring, commonly using machine learning and deep learning techniques. However, only a small number of studies examine farmers’ direct interaction with AI systems, including adoption, perceptions, and practical usage. This imbalance indicates that the literature remains largely technology-driven rather than farmer-centred. The review highlights important research gaps, particularly in farmer engagement, integrated farm management applications, and the translation of AI prototypes into scalable solutions. Future research should prioritize participatory approaches and context-sensitive AI systems to ensure that technological advances effectively support small-scale farmers and sustainable agricultural development.
Zimbini Coka, M. Monteiro, B. Jammer· Agriculture· 0 citations
The increasing global demand for water has emphasized the importance of irrigation systems capable of efficiently managing water use in precision agriculture. This paper presents a review of one hundred studies published between 2018 and 2025 that address the application of Internet of Things (IoT) technologies and computational approaches in irrigation management. The reviewed literature describes the use of sensor networks, wireless communication systems data-driven control mechanisms and automated decision-support tools aimed at improving irrigation practices while supporting crop production. The studies are organized according to the technologies and methods employed, including sensor-based data acquisition IoT-enabled monitoring platforms and intelligent irrigation control systems. A hybrid classification is also introduced to describe approaches that combine predictive modelling with adaptive irrigation strategies. Additionally, the reviewed works are discussed with respect to several system-related aspects such as water management objectives system architecture, and application scale. The paper also summarizes commonly used datasets and evaluation metrics reported in the literature and outlines key challenges identified by previous studies including data quality, communication reliability scalability and operational costs. Finally, the review highlights research directions that may contribute to the development of more efficient reliable and sustainable irrigation systems for precision agriculture
Nadia Zerguine, Aziza Ehmaid Omar, Z. Aliouat· International Journal of Com...· 0 citations
Sustainable agriculture is critical towards providing food security in the world and at the same time dealing with environmental degradation as well as shortages of resources. The loss of soil biodiversity, an increased resistance of pests and pathogens, and general decrease in crop yield have been contributed by traditional farming methods especially excessive use of agrochemicals. The deployment of cloud computing and remote sensing through the combination of artificial intelligence (AI) has allowed the expansion and scope of smart agriculture to reach cost-effective and responsive solutions to various farming settings. Nevertheless, the existing literature does not usually consider the potential of these technologies combined and does not provide a comprehensive approach to the implementation of these technologies in the synergy of their application. This review carries out extensive discussion of AI-based sustainable crop production by combining cloud computing and remote sensors. It discusses how smart sensors, satellite and aerial imaging platforms and cloud-based data infrastructures play a vital role in the development of crop health monitoring and predictive analytics. The major issues such as the heterogeneity of data, false positives, and the interpretability of the AI models are discussed critically. The areas of future research are also proposed to aid in creating multi-modal, robust, and interpretable AI systems that can propel sustainable, intelligent, and resilient agricultural ecosystems.
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MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
MIT News · Artificial Intelligence· news.mit.eduAug 27, 2026
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.