中图分类号：TP183 文献标志码：A DOI: 10.16157/j.issn.0258-7998.245285
中文引用格式： 杨翰琨，朱博威，张彦敏，等. 基于深度学习的植物病害图像识别算法综述[J]. 电子技术应用，2025，51(1)：1-7.
英文引用格式： Yang Hankun，Zhu Bowei，Zhang Yanmin，et al. Review of plant disease image recognition algorithms based on deep learning[J]. Application of Electronic Technique，2025，51(1)：1-7.

Review of plant disease image recognition algorithms based on deep learning

Abstract： Plant diseases pose a significant threat to agricultural production and food security, making timely and accurate identification and treatment critical. This article reviews the current status, challenges, and future directions of deep learning in plant disease identification. It begins by outlining the importance of plant diseases and the limitations of traditional identification methods, then explores the advantages of deep learning technologies and their application prospects in plant disease identification, especially the use of YOLO series models for real-time detection. Additionally, this article compares the performance of common deep learning algorithms in plant disease identification, and analyzes challenges such as dataset diversity, real-time performance, and catastrophic forgetting. Finally, it emphasizes the importance of continuous learning and model updates, and presents future research directions.

Key words : catastrophic forgetting；continuous learning；deep learning；plant disease identification；YOLO