Multimodal Graph-Based Domain Adaptation With Color and Texture Prompts for Plant Image Segmentation

This paper proposes a novel domain adaptation network to improve model accuracy in plant disease detection by leveraging structured image and text graphs. The proposed methodology constructs image graphs from segmented regions, including background, healthy, and disease regions. Class-specific text graphs are generated from prompts containing distinct color and texture attributes for each class. By aligning the structural information between image and text graphs, the network facilitates the effective transfer of class distinction capabilities from a source domain rich in data to a target domain with limited data, thereby addressing a key challenge of domain shift and data scarcity in agriculture. This approach incorporates text-image graph loss and image graph loss to enable comprehensive cross-domain class alignment, enhancing class distinction ability and ensuring consistent performance across diverse environmental conditions. Experimental evaluations across multiple source–target dataset pairs demonstrate improved class separation and stable mean IoU performance compared to existing approaches, validating the effectiveness of the proposed model in real-world agricultural scenarios. This method offers a promising pathway to enhance model adaptability and accuracy in disease detection, especially in complex and various agricultural environments.