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
Overall, AI offers strong potential to improve productivity and strengthen global food security under changing environmental conditions, and 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
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
A review of one hundred studies published between 2018 and 2025 that address the application of Internet of Things technologies and computational approaches in irrigation management aimed at improving irrigation practices while supporting crop production is presented.
Nadia Zerguine, Aziza Ehmaid Omar, Z. Aliouat· International Journal of Com...· 0 citations
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 is discussed, including the heterogeneity of data, false positives, and the interpretability of the AI models.
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
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.
