ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1524283
This article is part of the Research TopicOptimizing Deep Learning for Effective Plant Species Recognition and ConservationView all 14 articles
Constructing segmentation method for wheat powdery mildew using deep learning
Provisionally accepted
- Henan Academy of Agricultural Sciences (HNAAS), Zhengzhou, China
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Powdery mildew is an important factor affecting wheat yield and global food security, as well as a leading factor restricting the sustainable development of agriculture. Timely and accurate segmentation of wheat powdery mildew image is an important practical significance for disease resistant breeding and precise control. In this study, RSE-Swin Unet was proposed based on the Swin-Unet architecture to address the complex morphology of wheat powdery mildew lesions, blurred boundaries between lesions and non-lesions, and low segmentation accuracy. The method combines ResNet and SENet to solve the above problem. Firstly, the attention mechanism module SENet is introduced into Swin-Unet, which can effectively capture global and local features in images and extract more important information about powdery mildew. Secondly, the output of the SENet module add to the corresponding feature tensor of the decoder for subsequent decoder operations. Finally, in the deep bottleneck of Swin-Unet network, ResNet network layers are used to increase the expressive power of feature. The test results showed that in the experiment with the self-built wheat powdery mildew dataset, the proposed RSE-Swin Unet method achieved MIoU, mPA and Accuracy of 84.01%, 89.96%, 94.20%, respectively, which were 2.77%, 3.64%, and 2.89% higher than the original Swin-Unet method. In the wheat stripe rust dataset, the proposed RSE-Swin Unet method achieved MIOU, MPA, and Accuracy of 84.91%, 90.50%, and 96.88%, respectively, which were 4.64%, 5.38%, and 2.84% higher than those of the original Swin-Unet method. Compared with other mainstream deep learning methods U-Net, PSPNet, DeepLabV3+ and Swin-Unet, the proposed RSE Swin-Unet method can detect wheat powdery mildew and stripe rust image in challenging situation and has good computer vision processing and performance evaluation effects. The proposed method can accurately detect image of wheat powdery mildew and has good segmentation performance, which provides important support for the identification of resistance in wheat breeding materials.
Keywords: Wheat powdery mildew, deep learning, Swin-Unet, SENet, Resnet
Received: 07 Nov 2024; Accepted: 24 Apr 2025.
Copyright: © 2025 Zang, Wang, Zhao, Zhang, Wang, Zheng and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hecang Zang, Henan Academy of Agricultural Sciences (HNAAS), Zhengzhou, China
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