CAPBNet: Channel-Attentive Pyramid and Bottleneck-Guided Context Network for Plant Disease Segmentation

Accurate plant disease segmentation remains challenging due to complex natural backgrounds, diverse lesion appearances, and ambiguous disease boundaries. In this paper, we propose a Channel-Attentive Pyramid and Bottleneck-Guided Context Network for plant disease segmentation. The proposed network improves DeepLabV3-ResNet50 by recalibrating multi-scale atrous branch features, refining lesion-related spatial context, and combining pixel-wise supervision with region-level overlap optimization. Experiments on the PlantSeg115 dataset show that the proposed method achieves 44.52\% mIoU and 57.51\% mAcc, outperforming the DeepLabV3 baseline by 1.43 and 2.11 percentage points, respectively. These results validate the effectiveness of channel-attentive multi-scale representation and bottleneck-guided context refinement for plant disease semantic segmentation.