Abstract: In recent years，with the rapid development of computer vision technology，intelligent disease recognition systems based on digital image processing have demonstrated remarkable application potential in early diagnosis and precise control of crop diseases due to their efficiency and accuracy.This paper systematically reviews research progress in deep learning techniques for crop disease recognition.Through a comparative analysis with traditional machine learning methods，it highlights the technical advantages and application limitations of deep learning algorithms in disease feature extraction and classification.Furthermore，the paper analyzes the comprehensive technical workflow of deep learning in crop disease recognition and enumerates application cases utilizing mainstream network architectures. On this basis，the paper discusses key technical challenges faced by deep learning applications for crop disease recognition in complex field environments and provides perspectives on future research directions.The aim is to provide theoretical foundation and technical support for promoting the practical application of intelligent early warning and precise recognition technologies for crop diseases in modern agricultural production systems.

Key words: Deep learning, Crop disease recognition, Computer vision, Intelligent diagnosis, Agricultural informatization

摘要： 近年来，随着计算机视觉技术的快速发展，基于数字图像处理的智能化病害识别系统凭借其高效性、准确性等优势，在作物病害早期诊断与精准防控中展现出显著的应用前景。系统梳理了深度学习技术在作物病害识别领域的研究进展，通过与传统机器学习方法的对比分析，重点阐述了深度学习算法在病害特征提取、分类识别等方面的技术优势及应用局限性；分析了深度学习在作物病害识别中的全流程技术框架；列举了主流网络架构在植物病害识别中的应用案例。在此基础上，探讨了当前深度学习应用于田间复杂环境下作物病害识别所面临的关键技术挑战，并对未来研究方向进行了展望，以期为推动智能化病害早期预警与精准识别技术在现代农业生产体系中的实际应用提供理论依据和技术支撑。

关键词: 深度学习, 作物病害识别, 计算机视觉, 智能诊断, 农业信息化