Review of plant disease image recognition algorithms based on deep learning
Plant diseases pose a significant threat to agricultural production and food security,making timely and accurate identi-fication 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 meth-ods,then explores the advantages of deep learning technologies and their application prospects in plant disease identification,es-pecially 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.
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