关键词: 关键词: 樱桃病害检测, 目标检测, 注意力机制, 穿孔病, 褐腐病, 流胶病

Abstract: Abstract: To address the degradation in cherry disease detection accuracy caused by leaf occlusion and background clutter in complex agricultural environments, an improved variant of YOLOv8n named YOLO-APA was proposed. Three key enhancements centered on feature extraction and fusion were introduced. An Adaptive Focus Attention (AFA) mechanism with channel?spatial co?optimization was designed to enhance the model’s ability to recognize occluded regions. A Cross?position Pyramid Module (CPPM) was constructed, which integrated positional encoding with an adaptive gating mechanism to better fuse multi?scale features; the adaptive gating was used to extract local detail features and global context information respectively, and feature complementarity was achieved through channel?wise concatenation. An Adaptive Grouped Feature Fusion (AGFF) module was employed to dynamically group and fuse multi?scale features, aiming to improve the multi?target discrimination capability under complex backgrounds. Experiments were conducted on a self?constructed cherry disease image dataset. The results showed that YOLO?APA achieved a precision of 61.2%, a recall of 61.8%, and an mAP?.? of 63.4%, which were improvements of 5.0, 11.7, and 14.6 percentage points, respectively, over those of the original YOLOv8n.

Key words: Keywords: cherry disease detection, object detection, attention mechanism, shot hole disease, brown rot, gummosis