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.

中文翻译：

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人工智能赋能农业粮食体系：进展、挑战和机遇综述


随着世界人口的迅速增长，推动农业粮食体系转型，使其更具生产力、更高效、更安全和可持续，对于缓解潜在的粮食短缺至关重要。最近，深度学习 （DL） 等人工智能 （AI） 技术在语言、视觉、遥感 （RS） 和农业粮食系统应用等各个领域展示了其强大的能力。然而，人工智能对农业粮食体系的总体影响仍不明朗。在本文中，我们全面回顾了人工智能技术如何改变农业粮食体系并为现代农业食品产业做出贡献。首先，我们总结了农业粮食体系中的数据采集方法，包括采集、存储和加工技术。其次，我们回顾了农业粮食体系中人工智能方法的进展，特别是在农业、畜牧业和渔业领域，涵盖农业粮食分类、生长监测、产量预测和质量评估等主题。此外，我们还强调了利用人工智能改造现代农业粮食体系的潜在挑战和有前景的研究机会。我们希望这项调查可以为该领域的新手提供一个整体情况，并作为他们进一步研究的起点。项目网站 https://github.com/Frenkie14/Agrifood-Survey。