Research progress of machine learning in the chain of pest and disease monitoring, pesticide design, and precision application

Plant protection is a key link in agricultural production and plays a crucial role in ensuring national food and ecological security. However, traditional plant protection methods are confronted with challenges such as lagging pest and disease monitoring, long pesticide development cycles, extensive pesticide application, resource waste and environmental pollution. With its powerful data processing and pattern recognition capabilities, machine learning technology is systematically reshaping the entire chain of plant protection, enhancing its intelligence, automation and precision. This article, centered on "pest and disease monitoring, pesticide design and precision application", systematically reviews the research progress of machine learning in three key areas of plant protection: intelligent monitoring and early warning of pests and diseases, innovative design of pesticides and formulations, and precision pesticide application equipment. It focuses on the innovative applications of deep learning in image recognition, graph neural networks in molecular design, and reinforcement learning in decision optimization. In response to the challenges that machine learning faces in the field of plant protection, such as data, models, and interdisciplinary integration, this article looks forward to future research directions, aiming to provide reference for the intelligent, precise, and green transformation of plant protection patterns and practices.