Abstract: 【Objective】To propose an apple leaf disease detection method based on the YOLOv5s-GCB model,aiming to achieve a lightweight model while improving detection accuracy. 【Methods】On the basis of the YOLOv5s framework,Ghost convolution was introduced to reduce the computational load of convolution operations,thereby achieving a model lightweight;a coordinate attention（CA） mechanism was embedded in the Neck component to enhance the feature attention capability toward diseased spot regions on apple leaves;and a bidirectional weighted feature pyramid network（BiFPN） was adopted to optimize the multi-scale fusion of diseased spot features. The improved YOLOv5s-GCB model was trained and tested on a collected dataset of apple leaf disease images. 【Results】The improved YOLOv5s-GCB model demonstrates excellent performance in apple leaf disease detection tasks,with Precision,Recall,and mean Average Precision（mAP@0.5）reaching 90.7%,87.4%,and 93.4%,respectively. For the detection of Apple Alternaria blotch,gray leaf spot disease,and rust disease,the YOLOv5s-GCB model achieves the highest mAP@0.5 values of 93.8%,93.4%,and 93.0%,respectively. 【Conclusion】The improved YOLOv5s-GCB model not only possesses high-precision and high-speed detection capabilities but also features a compact model size,making it suitable for automated intelligent recognition of apple leaf diseases and demonstrating strong practical value.

Key words: YOLOv5s, Ghost, Coordinate attention（CA） mechanism, Bidirectional weighted feature pyramid network（BiFPN）, Apple leaf diseases, Detection