MSDP-SAM2-UNet: A Novel Multi-Scale and Dual-Path Model for Wheat Leaf Disease Segmentation Based on SAM2-UNet

Wheat is one of the world’s essential crops, and the presence of foliar diseases significantly affects both the yield and quality of wheat. Accurate identification of wheat leaf diseases is crucial. However, traditional segmentation models face challenges such as low segmentation accuracy, limiting their effectiveness in leaf disease control. To address these issues, this study proposes MSDP-SAM2-UNet, an efficient model for wheat leaf disease segmentation. Based on the SAM2-UNet network, we achieve multi-scale feature fusion through a dual-path multi-branch architecture, enhancing the model’s ability to capture global information and thereby improving segmentation performance. Additionally, we introduce an attention mechanism to strengthen residual connections, enabling the model to precisely distinguish targets from backgrounds and achieve greater robustness and higher segmentation accuracy. The experiments demonstrate MSDP-SAM2-UNet achieves outstanding performance across multiple metrics, including pixel accuracy (PA) of 94.02%, mean pixel accuracy (MPA) of 88.44%, mean intersection over union (MIoU) of 82.43%, frequency weighted intersection over union (FWIoU) of 90.73%, Dice coefficient of 81.76%, and precision of 81.63%. Compared to the SAM2-UNet, these metrics improved by 2.04%, 2.76%, 4.1%, 2.06%, 4.9%, and 3.6%, respectively. The results validate that MSDP-SAM2-UNet have tremendous segmentation performance and offer a novel perspective for wheat leaf disease segmentation.