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

TL;DR

This survey examines how artificial intelligence can transform agrifood systems by first detailing data acquisition, storage, and processing from diverse sources such as satellites, UAVs, and on-site sensors. It then reviews AI methods—traditional machine learning and deep learning—across agriculture, animal husbandry, and fishery, highlighting tasks like classification, growth monitoring, yield prediction, and quality assessment. The paper identifies cross-cutting challenges (data heterogeneity, model deployment, ethical risks) and outlines opportunities in foundation models, trustworthy AI, and AIoT to drive robust, scalable, and transparent agrifood AI solutions. By synthesizing ~200 studies and offering a framework for data-driven agriculture, the work aims to guide researchers, practitioners, and policymakers toward practical, sustainable AI-enabled agrifood systems.

Abstract

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.

Figures (19)

• Figure 1: The relationship between this survey and other surveys. The target domain of other surveys involved in Sec. 1.3 is represented as black points. The areas covered by white lines demonstrate the main theme of this survey.
• Figure 2: Diagram of the section structure for this survey.
• Figure 3: Example of optical images: Hyperspectral, Multi-spectral, and Panchromatic.
• Figure 4: UAVs equipped with multiple sensors simultaneously.
• Figure 5: Visualization of onsite devices' data acquisition process hasan2018detection. (a) Field photo of the onsite device tool. (b) The schematic of the tool and a sample image taken with the oblique-view camera.