DHB-DeepLabV3+: Boundary-Sensitive Context Aggregation Network for Agricultural Disease Segmentation

In agricultural disease images, lesions often exhibit large-scale variations, irregular shapes, gradual boundary transitions, and high texture similarity to surrounding backgrounds. These characteristics make models prone to insufficient lesion-body response, biased contextual discrimination, and unstable contour localization during segmentation. To address the fine-grained segmentation difficulties caused by imbalanced regional representation, semantic discrimination, and boundary constraints, this paper proposes DHB-DeepLabV3+, an agricultural disease image segmentation model. Based on lightweight feature extraction, the proposed model further organizes lesion segmentation beyond pixel-wise classification by jointly modeling lesion regions, contextual semantics, and boundary structures, thereby improving the completeness and boundary stability of disease regions in complex scenes. The proposed model was evaluated on three datasets, including a rice disease dataset, a leaf disease dataset, and the PlantSeg dataset. Across five independent runs, DHB-DeepLabV3+ achieved mean mIoU values of 88.45%, 82.52%, and 81.72% on the rice disease, leaf disease, and PlantSeg datasets, respectively, with corresponding standard deviations of 0.26, 0.35, and 0.38 percentage points. The lightweight version reduced the number of parameters to 6.67 M and the computational cost to 2.36 GFLOPs, and it was deployed on an RK3568 edge device for validation. Experimental results show that the proposed method achieves stable segmentation performance and has potential for edge-device application in complex agricultural disease segmentation scenarios.