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Generative artificial intelligence and ChatGPT in agriculture supply chain management: a systematic literature review and future research agenda

Mingxing Li et al.

British Food Journal2026https://doi.org/10.1108/bfj-05-2025-0720article
AJG 1ABDC B
Weight
0.37

Abstract

Purpose This paper presents a comprehensive overview of existing academic research on the application of Generative AI (Gen-AI) and ChatGPT within agricultural supply chains, highlighting current trends, leading publishers, thematic areas and proposing a future research agenda. Design/methodology/approach A bibliometric analysis was conducted using VOSviewer software on a sample of 79 articles retrieved from the Web of Science (WoS) and Scopus databases to identify emerging research topics. Findings The number of publications in this area has increased, and since 2023, academic interest in the application of Gen-AI and ChatGPT to agricultural supply chains has shown a consistent upward trend. Key areas of focus include enhancing prediction accuracy, coordination, automation, employee performance, optimizing supply chain management and improving overall business outcomes. However, concerns also exist, like hallucination, data privacy, security risks, lack of accuracy, copyright issues and ethical implications. Consequently, the risk of ungoverned Gen-AI outputs without human oversight potentially disrupting operations underscores the need for robust governance frameworks to ensure its reliable integration in agricultural contexts. Practical implications Gen-AI and ChatGPT provide several advantages for agricultural supply chains. They enhance forecasting capabilities and coordination among partners, automate stakeholder interactions to increase efficiency, reduce manual tasks to improve employee performance and support data-driven decision-making through advanced analytics, ultimately strengthening supply chain management. Originality/value This study offers a structured and in-depth examination of the Gen-AI and ChatGPT phenomenon in agricultural supply chains. Moreover, it contributes to the literature by identifying existing research gaps and suggesting potential directions for future investigation.

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https://doi.org/https://doi.org/10.1108/bfj-05-2025-0720

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@article{mingxing2026,
  title        = {{Generative artificial intelligence and ChatGPT in agriculture supply chain management: a systematic literature review and future research agenda}},
  author       = {Mingxing Li et al.},
  journal      = {British Food Journal},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/bfj-05-2025-0720},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.