作物病害严重威胁全球农业生产与粮食安全。为保障作物产量稳定、实现可持续的植物保护，开发高效且无损的作物健康监测技术是尤为重要的。随着传感系统与计算方法的持续进步，构建智能化的农业病害监测体系展现出广阔前景。本文从传感器与系统、关键方法及实际应用三个维度，系统评述了农业病害监测的现有研究成果。文中详细剖析了不同传感系统的功能特性，探讨了病害检测、预测与预警的核心技术，并考察了其在农业实践中的应用成效。同时，本文指出了该领域面临的主要挑战，特别是在实时检测技术、预警模型构建与数据共享机制等方面存在的瓶颈问题。最后，探索了将作物病害监测与大数据、人工智能（AI）和物联网（IoT）结合的创新方向和应用前景。这些研究进展有望为作物病害监测领域的理论创新和实践应用开辟新路径。

Crop diseases represent a significant threat to global agricultural productivity and food security. The advancement of non-invasive and efficient crop health monitoring technologies is critical for sustainable crop protection and yield stability. The continuous progress of sensing systems and computational methodologies offers promising avenues for developing intelligent agricultural disease monitoring systems. This review systematically evaluates existing research from three dimensions: sensors and systems, methods and algorithms, and applications. It provides an in-depth analysis of the roles of different sensors and systems, discusses key methods and techniques, prediction, and early warning, and explores their applications in real-world agricultural scenarios. Furthermore, this paper identifies the main challenges in agricultural disease surveillance research, particularly in the development of real-time detection techniques, the construction of early-warning models, and the promotion of data sharing and collaboration. Finally, innovative directions and application prospects are explored for integrating crop disease monitoring with big data, artificial intelligence (AI), and the Internet of Things (IoT). These research advances are expected to open new avenues for theoretical innovation and practical applications in crop disease monitoring.