mlsanityData to 7 Oct 2026

Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities

arXiv 2305.01899 ↗First posted 3 May 2023cs.AI cs.CY eess.IVcited by 1

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Abstract (arXiv)

With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages. Recently, artificial intelligence (AI) techniques such as deep learning (DL) have demonstrated their strong abilities in various areas, including language, vision, remote sensing (RS), and agrifood systems applications. However, the overall impact of AI on agrifood systems remains unclear. In this paper, we thoroughly review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. Firstly, we summarize the data acquisition methods in agrifood systems, including acquisition, storage, and processing techniques. Secondly, we present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery, covering topics such as agrifood classification, growth monitoring, yield prediction, and quality assessment. Furthermore, we highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI. We hope this survey could offer an overall picture to newcomers in the field and serve as a starting point for their further research. The project website is https://github.com/Frenkie14/Agrifood-Survey.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 236 references that are in the map. First posted on arXiv 3 May 2023.

Builds on

30 of 236 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Agent-Based Post-Hoc Correction of Agricultural Yield Forecastscited by 0