DOI: 10.12688/f1000research.184726.1 ISSN: 2046-1402

Twenty-Five Years of Research on Artificial Intelligence-Driven Farmers' Decision-Making: A Bibliometric and Science Mapping Analysis of Intellectual Structure, Thematic Evolution, and Future Research Directions

Rayana Rayana, Richan Cahya Pribadi, Muhamad Risqiwahid, Dzilalin Najmi, Valentina Novita Bere, Windri Widyaningsih, Bangkit Wiguna, Fedelfia Kambu, Argunia Cristal Kurni, Arfiana Arfiana
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Background Artificial intelligence (AI) is transforming agricultural decision-making through data-driven approaches to resource management, production planning, and risk mitigation. As digital agriculture continues to expand, research on AI-supported farmers’ decision-making has increased across multiple disciplines. However, the existing body of knowledge remains fragmented, limiting a comprehensive understanding of its intellectual foundations, thematic structure, and emerging research directions. This study systematically maps the scientific landscape of AI-driven farmers’ decision-making and identifies its key contributors, thematic domains, and future research priorities. Methods A bibliometric and science-mapping approach was employed using the Scopus database. Following the PRISMA protocol, 217 English-language articles and review papers published between 2000 and 2025 were selected using the search query: “Farmers” AND “Decision-Making” AND “Artificial Intelligence”. Performance analysis and thematic mapping were conducted to examine publication trends, influential contributors, geographical distribution, conceptual structures, and thematic evolution. Results The findings reveal a rapidly expanding field, with an annual growth rate of 19.37%. India emerged as the most productive contributor, while Spain and Germany demonstrated the highest citation impact. Four principal thematic domains were identified: AI-enabled precision agriculture and decision support; smart resource monitoring and water management; environmental modelling and resource governance; and sustainable agricultural systems. Thematic evolution indicates a shift from environmental simulation and resource optimisation towards data-intensive agricultural systems supported by machine learning, the Internet of Things (IoT), forecasting, crop-yield prediction, and explainable artificial intelligence. The increasing prominence of explainable AI reflects growing attention to transparency, interpretability, and user-centred design.

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