Jul 2026· Agriculture· Vol 16, pp. 1518· 0 citations
TL;DR
A systematic literature review of the applications, impacts, and challenges of AI in small-scale agriculture highlights important research gaps, particularly in farmer engagement, integrated farm management applications, and the translation of AI prototypes into scalable solutions.
Abstract
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.
The application of Artificial Intelligence (AI) technologies in precision agriculture has potential to enhance agricultural productivity and farm management, especially in smallholder settings. The research seeks to explore the use, benefits and barriers of AI technologies in smallholder farming. Through a systematic review of 50 peer-reviewed research papers sourced from major academic databases, the study reveals common themes focusing on the application of technologies, barriers to adoption, and facilitating factors in the use of AI in smallholder farming. This review highlights the major applications of AI in agriculture, such as machine learning-based disease diagnosis, yield modeling, smart irrigation, and decision support. Analysis suggests that these tools hold great promise for boosting farm productivity through early disease diagnosis, optimal use of agricultural inputs, data-driven decision making, and increased sustainability. But adoption of AI technologies by smallholder farmers is highly variable. The research also shows that the adoption is heavily dependent on behavioural and socio-economic factors such as farmers’ attitudes, trust, digital skills, infrastructure, and cost. Furthermore, implementation is constrained by structural factors such as lack of connectivity, skills, extension services, ethical considerations and data management. Likewise, enablers such as human-centred design, improved advisory and extension services, policies, and inclusive and collaborative innovation systems can also boost adoption and effectiveness. This review provides a holistic view of AI-driven precision agriculture by synthesising evidence on technical performance, socio-economic and policy considerations. It offers a conceptual basis for future empirical studies, and highlights the importance of context-sensitive, inclusive and farmer-friendly AI technologies to drive equitable and sustainable digital agriculture.
Sandip Satpati· Discover Global Society· 0 citations
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
This study aims to examine how artificial intelligence (AI) driven sustainable agriculture practices contribute to environmental sustainability among farmers in Ghana. It also examines the mediating role of market-oriented strategies in this relationship.
The research used a quantitative approach and used structural equation modeling to investigate the impact of AI adoption on reducing environmental impact, mediated by market orientation strategies. The data were collected from 211 individual farmers who actively use/adopted an AI in their farming.
The study identifies that the adoption of AI technologies has a strong and significant impact on improving crop management, resource efficiency and reducing carbon footprint. However, the mediating effect of market-oriented strategies showed mixed results for access to market and price mechanisms.
While the study offers a valuable conceptual framework, its generalizability may be improved with a bigger sample. Future studies are encouraged to conduct the study in other jurisdictions to validate the proposed relationships.
This study contributes to the literature by bridging the gap between AI technological innovations and market strategy within the sustainability discourse. This paper goes beyond the techno-centric approach to reducing environmental impact and presents a holistic model that emphasizes market-oriented strategies as a key enabler of Adopting AI in reducing environmental impact within the agriculture sector.
M. A. Mahmoud, Charles Blankson· Management Research Review· 0 citations
A descriptive bibliometric and thematic synthesis of 31,452 publications on the application of artificial intelligence in agriculture in Scopus from 2020 to 2025 reveals a concentrated research landscape dominated by production-oriented applications, while interdisciplinary and emerging AI domains remain relatively fragmented.
Jobelle J. Capilitan, Abigael L. Balbin, Junrie B. Matias· AgriEngineering· 1 citation
Agriculture remains the backbone of India’s economy, employing nearly half of the population but contributing only about 18% to GDP. Most Indian farmers are smallholders managing fragmented land under uncertain climatic conditions. To address these challenges, this paper presents AnnadattaSaathi, an AI-powered decision support ecosystem designed for crop selection and socio-economic yield optimization. The proposed system combines a Random Forest–based recommendation model (achieving 99.31% accuracy in experimental evaluation) with a scalable MERN–Flask architecture to provide practical field-level recommendations. To improve usability and trust, the system integrates Explainable AI techniques such as SHAP and LIME along with a multilingual conversational interface powered by GPT-4o. Unlike many existing research prototypes, AnnadattaSaathi focuses on real-world deployment by combining machine learning, IoT sensing, and user-friendly advisory tools. The system aims to support Indian farmers with transparent, localized, and actionable insights for precision agriculture.
D. Datt, D. Kumari, Dr. Hemkant Kulshrestha et al.· Genetics and Molecular Resea...· 0 citations
Artificial Intelligence (AI), as one of the most advanced technologies of the present era, holds significant potential for transformation across various sectors, particularly agriculture. This research employs a survey method to analyze the role of AI in the agricultural sector of Afghanistan (Balkh) and examine the existing challenges and opportunities. The designed questionnaire, consisting of 21 diverse questions, collected information from farmers, agricultural experts, and IT professionals. These questions assessed participants' awareness and attitudes towards AI, its applications in agriculture, and the challenges in adopting this technology. Findings indicate that most respondents (over 80%) perceive the agricultural status of the region as progressive and consider AI effective in addressing food crises and fostering economic growth. However, main challenges identified include lack of awareness, climatic changes, and shortage of specialized workforce. Furthermore, over 90% of farmers demand empowerment and training in AI applications for improving product quality. Results suggest that despite challenges, attitudes towards the future of AI in agriculture are positive, and planning for education and infrastructure development can play a key role in adopting this technology.
Besmullah Musammim, Mohammad Kabir Ahmadi· International Journal of Nat...· 0 citations
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