Suchang Yin

Driven by artificial intelligence, multi-source sensing, agricultural robots and big data technologies, global agriculture is rapidly upgrading from precision agriculture and agriculture 4.0 to agriculture 5.0. Artificial intelligence has evolved from a single diagnostic tool to an intelligent system that integrates the “perception-decision-execution” process throughout. It is widely applied in crop phenotype analysis, remote sensing monitoring, yield prediction, and autonomous operation of intelligent equipment, etc. This article takes the framework of “intelligent perception-cognitive decision-autonomous execution” to systematically review the core technologies, typical applications, and frontier directions of agricultural artificial intelligence. It focuses on introducing the progress of key technologies such as three-dimensional phenotype, hyperspectral remote sensing, multimodal fusion, and causal machine learning, as well as their value in improving resource utilization efficiency, enhancing climate resilience, and supporting field precision management. At the same time, it points out that current agricultural AI still faces practical bottlenecks such as insufficient generalization ability of models, scarce data and high annotation costs, difficulties in edge deployment, barriers in multi-source data integration, and weak interpretability and engineering reliability. Future research will focus on the construction of closed-loop autonomous farms, the collaboration of agricultural large models and intelligent agents, the construction of data centers and AI and data infrastructure, and the development of green and low-cost AI research. This will provide support for the technological innovation and industrialization implementation of agricultural artificial intelligence.