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The paper introduces SGPer, a Semantic-Geometric Prior Synergization framework, to address the challenge of wheat disease segmentation with limited data by leveraging pre-trained DINOv2 and SAM. SGPer uses DINOv2 to provide robust category-aware semantic priors which are then converted into spatial prompts to guide SAM for precise disease boundary localization. By inserting disease-sensitive adapters into DINOv2 and SAM and dynamically filtering prompts based on semantic consistency and mask confidence, SGPer achieves state-of-the-art segmentation performance, particularly in data-constrained scenarios.
Achieve SOTA wheat disease segmentation with limited data by cleverly combining the semantic power of DINOv2 with the geometric precision of SAM.
Wheat disease segmentation is fundamental to precision agriculture but faces severe challenges from significant intra-class temporal variations across growth stages. Such substantial appearance shifts make collecting a representative dataset for training from scratch both labor-intensive and impractical. To address this, we propose SGPer, a Semantic-Geometric Prior Synergization framework that treats wheat disease segmentation under limited data as a coupled task of disease-specific semantic perception and disease boundary localization. Our core insight is that pretrained DINOv2 provides robust category-aware semantic priors to handle appearance shifts, which can be converted into coarse spatial prompts to guide SAM for the precise localization of disease boundaries. Specifically, SGPer designs disease-sensitive adapters with multiple disease-friendly filters and inserts them into both DINOv2 and SAM to align their pretrained representations with disease-specific characteristics. To operationalize this synergy, SGPer transforms DINOv2-derived features into dense, category-specific point prompts to ensure comprehensive spatial coverage of all disease regions. To subsequently eliminate prompt redundancy and ensure highly accurate mask generation, it dynamically filters these dense candidates by cross-referencing SAM's iterative mask confidence with the category-specific semantic consistency derived from DINOv2. Ultimately, SGPer distills a highly informative set of prompts to activate SAM's geometric priors, achieving precise and robust segmentation that remains strictly invariant to temporal appearance changes. Extensive evaluations demonstrate that SGPer consistently achieves state-of-the-art performance on wheat disease and organ segmentation benchmarks, especially in data-constrained scenarios.
