  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.
