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

Author

Listed:
  • 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
    Macquarie Business School)

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.

Suggested Citation

  • Sneha Kumari & V. G. Venkatesh & Felix Ter Chian Tan & S. Vijayakumar Bharathi & M. Ramasubramanian & Yangyan Shi, 2025. "Application of machine learning and artificial intelligence on agriculture supply chain: a comprehensive review and future research directions," Annals of Operations Research, Springer, vol. 348(3), pages 1573-1617, May.
  • Handle: RePEc:spr:annopr:v:348:y:2025:i:3:d:10.1007_s10479-023-05556-3
    DOI: 10.1007/s10479-023-05556-3
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    References listed on IDEAS

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    1. Jingsi Huang & Jie Song, 2018. "Optimal inventory control with sequential online auction in agriculture supply chain: an agent-based simulation optimisation approach," International Journal of Production Research, Taylor & Francis Journals, vol. 56(6), pages 2322-2338, March.
    2. Loet Leydesdorff & Lutz Bornmann & Rüdiger Mutz & Tobias Opthof, 2011. "Turning the tables on citation analysis one more time: Principles for comparing sets of documents," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(7), pages 1370-1381, July.
    3. Leo Egghe, 2006. "Theory and practise of the g-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 131-152, October.
    4. Garfield, Eugene, 2009. "From the science of science to Scientometrics visualizing the history of science with HistCite software," Journal of Informetrics, Elsevier, vol. 3(3), pages 173-179.
    5. Deepa Mishra & Angappa Gunasekaran & Thanos Papadopoulos & Stephen J. Childe, 2018. "Big Data and supply chain management: a review and bibliometric analysis," Annals of Operations Research, Springer, vol. 270(1), pages 313-336, November.
    6. Shahriar Akter & Samuel Fosso Wamba, 2019. "Big data and disaster management: a systematic review and agenda for future research," Annals of Operations Research, Springer, vol. 283(1), pages 939-959, December.
    7. Fielke, Simon & Taylor, Bruce & Jakku, Emma, 2020. "Digitalisation of agricultural knowledge and advice networks: A state-of-the-art review," Agricultural Systems, Elsevier, vol. 180(C).
    8. Raffaele Cioffi & Marta Travaglioni & Giuseppina Piscitelli & Antonella Petrillo & Fabio De Felice, 2020. "Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions," Sustainability, MDPI, vol. 12(2), pages 1-26, January.
    9. Antonio Manuel Ciruela-Lorenzo & Ana Rosa Del-Aguila-Obra & Antonio Padilla-Meléndez & Juan José Plaza-Angulo, 2020. "Digitalization of Agri-Cooperatives in the Smart Agriculture Context. Proposal of a Digital Diagnosis Tool," Sustainability, MDPI, vol. 12(4), pages 1-15, February.
    10. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Machine Learning Methods for Demand Estimation," American Economic Review, American Economic Association, vol. 105(5), pages 481-485, May.
    11. Sigifredo Laengle & José M. Merigó & Nikunja Mohan Modak & Jian-Bo Yang, 2020. "Bibliometrics in operations research and management science: a university analysis," Annals of Operations Research, Springer, vol. 294(1), pages 769-813, November.
    12. Anirut Kantasa-ard & Maroua Nouiri & Abdelghani Bekrar & Abdessamad Ait el cadi & Yves Sallez, 2021. "Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand," International Journal of Production Research, Taylor & Francis Journals, vol. 59(24), pages 7491-7515, December.
    13. Brandenburg, Marcus & Govindan, Kannan & Sarkis, Joseph & Seuring, Stefan, 2014. "Quantitative models for sustainable supply chain management: Developments and directions," European Journal of Operational Research, Elsevier, vol. 233(2), pages 299-312.
    14. Alfons Weersink & Evan Fraser & David Pannell & Emily Duncan & Sarah Rotz, 2018. "Opportunities and Challenges for Big Data in Agricultural and Environmental Analysis," Annual Review of Resource Economics, Annual Reviews, vol. 10(1), pages 19-37, October.
    15. George Baryannis & Sahar Validi & Samir Dani & Grigoris Antoniou, 2019. "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2179-2202, April.
    16. Borodin, Valeria & Bourtembourg, Jean & Hnaien, Faicel & Labadie, Nacima, 2016. "Handling uncertainty in agricultural supply chain management: A state of the art," European Journal of Operational Research, Elsevier, vol. 254(2), pages 348-359.
    17. Abhishek Behl & Pankaj Dutta, 2019. "Humanitarian supply chain management: a thematic literature review and future directions of research," Annals of Operations Research, Springer, vol. 283(1), pages 1001-1044, December.
    18. Kamble, Sachin S. & Gunasekaran, Angappa & Gawankar, Shradha A., 2020. "Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications," International Journal of Production Economics, Elsevier, vol. 219(C), pages 179-194.
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