IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v348y2025i3d10.1007_s10479-023-05556-3.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-023-05556-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-023-05556-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Leo Egghe, 2006. "Theory and practise of the g-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 131-152, October.
    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 Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(7), pages 1370-1381, July.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiaofei Yang & Qiao Li & Honghui Li & Hao Zhou & Jinyan Zhang & Xueliang Fu, 2025. "An Innovative Inversion Method of Potato Canopy Chlorophyll Content Based on the AFFS Algorithm and the CDE-EHO-GBM Model," Agriculture, MDPI, vol. 15(11), pages 1-32, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abbate, Stefano & Centobelli, Piera & Cerchione, Roberto, 2023. "The digital and sustainable transition of the agri-food sector," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    2. Sachin Modgil & Rohit Kumar Singh & Cyril Foropon, 2022. "Quality management in humanitarian operations and disaster relief management: a review and future research directions," Annals of Operations Research, Springer, vol. 319(1), pages 1045-1098, December.
    3. Josip Marić & Carlos Galera-Zarco & Marco Opazo-Basáez, 2022. "The emergent role of digital technologies in the context of humanitarian supply chains: a systematic literature review," Annals of Operations Research, Springer, vol. 319(1), pages 1003-1044, December.
    4. Surajit Bag & Shivam Gupta & Lincoln Wood, 2022. "Big data analytics in sustainable humanitarian supply chain: barriers and their interactions," Annals of Operations Research, Springer, vol. 319(1), pages 721-760, December.
    5. Rodolfo Modrigais Strauss Nunes & Susana Carla Farias Pereira, 2022. "Intellectual structure and trends in the humanitarian operations field," Annals of Operations Research, Springer, vol. 319(1), pages 1099-1157, December.
    6. Emilia Vann Yaroson & Soumyadeb Chowdhury & Sachin Kumar Mangla & Prasanta Kumar Dey, 2024. "Unearthing the interplay between organisational resources, knowledge and industry 4.0 analytical decision support tools to achieve sustainability and supply chain wellbeing," Annals of Operations Research, Springer, vol. 342(2), pages 1321-1368, November.
    7. Fosso Wamba, Samuel & Queiroz, Maciel M. & Trinchera, Laura, 2024. "The role of artificial intelligence-enabled dynamic capability on environmental performance: The mediation effect of a data-driven culture in France and the USA," International Journal of Production Economics, Elsevier, vol. 268(C).
    8. Shahriar Akter & Saradhi Motamarri & Shahriar Sajib & Ruwan J. Bandara & Shlomo Tarba & Demetris Vrontis, 2024. "Theorising the Microfoundations of analytics empowerment capability for humanitarian service systems," Annals of Operations Research, Springer, vol. 335(3), pages 989-1013, April.
    9. Amine Belhadi & Venkatesh Mani & Sachin S. Kamble & Syed Abdul Rehman Khan & Surabhi Verma, 2024. "Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation," Annals of Operations Research, Springer, vol. 333(2), pages 627-652, February.
    10. Rameshwar Dubey & David J. Bryde & Cyril Foropon & Gary Graham & Mihalis Giannakis & Deepa Bhatt Mishra, 2022. "Agility in humanitarian supply chain: an organizational information processing perspective and relational view," Annals of Operations Research, Springer, vol. 319(1), pages 559-579, December.
    11. Lutz Bornmann & Werner Marx, 2014. "How to evaluate individual researchers working in the natural and life sciences meaningfully? A proposal of methods based on percentiles of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 487-509, January.
    12. Bornmann, Lutz & Marx, Werner, 2012. "HistCite analysis of papers constituting the h index research front," Journal of Informetrics, Elsevier, vol. 6(2), pages 285-288.
    13. Sundarakani, Balan & Ajaykumar, Aneesh & Gunasekaran, Angappa, 2021. "Big data driven supply chain design and applications for blockchain: An action research using case study approach," Omega, Elsevier, vol. 102(C).
    14. Himanshu Gupta & Manjeet Kharub & Kumar Shreshth & Ashwani Kumar & Donald Huisingh & Anil Kumar, 2023. "Evaluation of strategies to manage risks in smart, sustainable agri‐logistics sector: A Bayesian‐based group decision‐making approach," Business Strategy and the Environment, Wiley Blackwell, vol. 32(7), pages 4335-4359, November.
    15. Paula Camargo Fiorini & Charbel Jose Chiappetta Jabbour & Ana Beatriz Lopes de Sousa Jabbour & Gary Ramsden, 2022. "The human side of humanitarian supply chains: a research agenda and systematization framework," Annals of Operations Research, Springer, vol. 319(1), pages 911-936, December.
    16. Görkem Sariyer & Mustafa Gokalp Ataman & Sachin Kumar Mangla & Yigit Kazancoglu & Manoj Dora, 2023. "Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations," Annals of Operations Research, Springer, vol. 328(1), pages 1073-1103, September.
    17. Anas Iftikhar & Imran Ali & Ahmad Arslan & Shlomo Tarba, 2024. "Digital Innovation, Data Analytics, and Supply Chain Resiliency: A Bibliometric-based Systematic Literature Review," Annals of Operations Research, Springer, vol. 333(2), pages 825-848, February.
    18. Robert Finger, 2023. "Digital innovations for sustainable and resilient agricultural systems," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(4), pages 1277-1309.
    19. Ingram, Julie & Maye, Damian & Bailye, Clive & Barnes, Andrew & Bear, Christopher & Bell, Matthew & Cutress, David & Davies, Lynfa & de Boon, Auvikki & Dinnie, Liz & Gairdner, Julian & Hafferty, Caitl, 2022. "What are the priority research questions for digital agriculture?," Land Use Policy, Elsevier, vol. 114(C).
    20. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2020. "The Unknown Potential of Blockchain for Sustainable Supply Chains," Sustainability, MDPI, vol. 12(22), pages 1-16, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:348:y:2025:i:3:d:10.1007_s10479-023-05556-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.