PlantFormer: a precise plant disease segmentation network with interactive backbone and global-anisotropic context...

Precise plant disease segmentation in real-world agricultural environments presents challenges that general-purpose models often fail to address, primarily due to the anisotropic spread of lesions, blurred biological boundaries, and severe background dominance. To overcome these bottlenecks, this paper proposes PlantFormer, an end-to-end network that effectively integrates and adapts advanced architectural components to address these domain-specific issues. Specifically, PlantFormer employs an InteractSwin Backbone with a Cross-Level Fusion (CLF) module to preserve early pathological details. To model highly directional disease propagation, a GlobalAnisotropic Context Aggregation (GACA) neck utilizing strip pooling is introduced. Furthermore, a Semantic-Guided Fusion (SGF) decoder acts as a feature "boundary purifier" to suppress field noise, while a decoupled boundary-aware loss function explicitly shifts the optimization focus from healthy leaf regions to subtle necrotic transition zones. Comprehensive experiments demonstrate the effectiveness of our approach: PlantFormer achieves 41.78% mIoU on the complex PlantSeg dataset (unstructured field conditions) and 93.54% mIoU on the structured NLB dataset (vein-aligned lesions). It outperforms generalist models such as DeepLabV3+ and Segformer in key metrics like mIoU and mAcc. Despite these promising results, limitations remain, particularly regarding performance in scenarios with high-density, early-stage disease outbreaks, which will be the focus of future work.