Segmentation Method for Wheat Stripe Rust Based on Improved Swin-Unet
Stripe rust is an important factor affecting wheat yield and food security,and accurate seg-mentation of wheat stripe rust images is an important means of computer-aided precision control.A wheat stripe rust image segmentation method based on improved Swin-Unet was proposed in this study to address the prob-lems of complex lesion morphology,blurred boundaries between lesions and non-lesions,and low segmentation accuracy in wheat stripe rust image.The method enhanced the model's ability to express stripe rust features by introducing SENet and ResNet modules into Swin-Unet.The experimental results showed that the improved Swin-Unet had precision rates of 99.24%,82.32%and 94.36%for background,spore and leaf,respectively,and could segment background,spore and leaf images in challenging situations,so it had better computer vi-sion processing and segmentation evaluation effects.The overall segmentation accuracy,average intersection to union ratio and average pixel accuracy of improved Swin-Unet were 96.88%,84.91%and 90.50%,respective-ly,which were 2.84,4.64 and 5.38 percentage points higher than those of Swin-Unet.Compared with other network models such as U-Net、PSPNet、DeepLabV3+and Swin-Unet,the improved Swin-Unet had the best segmentation performance.The method proposed in this study could accurately detect and segment wheat stripe rust features,providing technical support for automatic detection and early prevention of wheat stripe rust in complex field environments.
国家科技期刊平台
NSTL
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