Application of Machine Learning and Artificial Intelligence on Agriculture Supply Chain: A Comprehensive Review and Future Research Directions

S. Kumari, V.G. Venkatesh, F.T.C. Tan, S.V. Bharathi, M. Ramasubramanian and Y. Shi
Additional contact information
S. Kumari: Symbiosis International (Deemed University)
V.G. Venkatesh: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School
F.T.C. Tan: University of New South Wales [Kensington]
S.V. Bharathi: Symbiosis International (Deemed University)
M. Ramasubramanian: Loyola Institute of Business Administration
Y. Shi: Macquarie University [Sydney]

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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. \textcopyright 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: Agriculture; Agriculture supply chain; Artificial intelligence; Bibliometric analysis; Deep learning; Machine learning; Random forests (search for similar items in EconPapers)
Date: 2023-09-05
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Citations: View citations in EconPapers (2)

Published in Annals of Operations Research, 2023, ⟨10.1007/s10479-023-05556-3⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04433057

DOI: 10.1007/s10479-023-05556-3

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