The role of Artificial Intelligence in Agri-Food Supply Chains: A Systematic Literature Review

Khan, Bilal (2022) The role of Artificial Intelligence in Agri-Food Supply Chains: A Systematic Literature Review. [Dissertation (University of Nottingham only)]

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Abstract

The agri-food sector has the most important role in sustaining all human life. Challenges such as rapid population growth, climate change, and competition for resources are a major threat to global food security. As a result, in the past few years, digital technologies such as artificial intelligence, blockchain and IoT have progressively been utilized for digitalizing agri-food supply chains (AFSCs). A key digital technology, artificial intelligence, has been employed to address several problems in AFSCs such as yield improvement, productivity, enhancing food safety & security, and building greater efficiency across the whole AFSC. The current study presents a systematic literature review (SLR) on the role of artificial intelligence across all the phases of AFSC such as pre-production, production, processing, storage, distribution, retail and the broader AFSC. The SLR reviews a total of 106 research papers based on the role of artificial intelligence across all the AFSC stages. The research highlights how various prominent artificial intelligence technologies are being employed across different AFSC stages and the benefits they are generating. The SLR provides a holistic overview of the current state of literature regarding the role of artificial intelligence across the AFSC and how it is adding value at each stage of the AFSC. Furthermore, the role of artificial intelligence in transforming the traditional AFSCs has also been discussed. Finally, the SLR provides key future research directions for researchers based on the analysis of literature.

Item Type: Dissertation (University of Nottingham only)
Keywords: Agri-food Supply Chains, Artificial Intelligence, Machine Learning, Systematic Literature Review
Depositing User: Khan, Bilal
Date Deposited: 20 Jun 2023 15:41
Last Modified: 20 Jun 2023 15:41
URI: https://eprints.nottingham.ac.uk/id/eprint/69966

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