中圖分類號：TP183 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.245285
中文引用格式： 楊翰琨，朱博威，張彥敏，等. 基于深度學習的植物病害圖像識別算法綜述[J]. 電子技術應用，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