Application of machine learning and artificial intelligence on agriculture supply chain: a comprehensive review and future research directions

Sneha Kumari (), V. G. Venkatesh (), Felix Ter Chian Tan (), S. Vijayakumar Bharathi (), M. Ramasubramanian () and Yangyan Shi ()
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Sneha Kumari: Symbiosis International (Deemed University)
V. G. Venkatesh: EM Normandie Business School, Metis Lab
Felix Ter Chian Tan: University of New South Wales
S. Vijayakumar Bharathi: Symbiosis International (Deemed University)
M. Ramasubramanian: Loyola Institute of Business Administration
Yangyan Shi: Chongqing Jiao Tong University

Annals of Operations Research, 2025, vol. 348, issue 3, No 15, 1573-1617

Abstract: Abstract Agriculture has transitioned from traditional to contemporary practices because of technological transformation. Powered by digital technologies and analytics such as machine learning and artificial intelligence, the application of analytics has become an emerging topic in the agriculture supply chain. The study has used bibliometric and visualization tools followed by a taxonomy of the research manuscripts. The results confirm that the publication trend has increased as ASC has been demanding the application of AI and ML. The results of the geographical mapping, journal statistics, keyword analysis, network analysis, affiliation statistics, citation analysis, keywords map, co-occurrences and factor analysis reveal the transformation of ASC towards precision agriculture, deep learning, reinforcement learning, food safety and food supply chain. Based on the results and discussions, the work provided a roadmap for future studies on emerging research themes. It contributes to the literature by discussing the scope for machine learning in the coming years and, more importantly, identifying the research clusters and future research directions. The concept has been gaining momentum in recent years, and therefore, it has become necessary to categorize diverse types of research output and study the research trend in the agriculture supply chain.

Keywords: Machine learning; Artificial intelligence; Agriculture supply chain; Bibliometric analysis; Agriculture; Deep learning; Random forests (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-023-05556-3

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