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.

翻译：随着世界人口的快速增长，变革我们的农业食品系统以提升其生产力、效率、安全性和可持续性，对于缓解潜在的食物短缺至关重要。近年来，深度学习等人工智能技术在语言、视觉、遥感及农业食品系统应用等多个领域展现出强大能力。然而，人工智能对农业食品系统的整体影响仍不明确。本文系统综述了人工智能技术如何变革农业食品系统并推动现代农业食品产业发展。首先，我们总结了农业食品系统中的数据获取方法，包括数据采集、存储与处理技术。其次，我们回顾了人工智能方法在农业食品系统中（特别是种植业、畜牧业和水产业）的进展，涵盖农产品分类、生长监测、产量预测及质量评估等主题。此外，我们指出了利用人工智能变革现代农业食品系统的潜在挑战与重要研究机遇，期望本综述能为新入行者提供整体图景，并作为其深入研究的起点。