Vision-Language Semantic Grounding for Multi-Domain Crop-Weed Segmentation

Description

Fine-grained crop-weed segmentation is essential for enabling targeted herbicide application in precision agriculture. However, existing deep learning models struggle to generalize across heterogeneous agricultural environments due to reliance on dataset-specific visual features. We propose Vision-Language Weed Segmentation (VL-WS), a novel framework that addresses this limitation by grounding pixel-level segmentation in semantically aligned, domain-invariant representations. Our architecture em

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