Constructing segmentation method for wheat powdery mildew using deep learning.

Journal: Frontiers in plant science
Published Date: May 26, 2025

Abstract

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 abovementioned 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 values of 84.01%, 89.96%, and 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 values 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 a challenging situation and has good computer vision processing and performance evaluation effects. The proposed method can accurately detect the image of wheat powdery mildew and has good segmentation performance, which provides important support for the identification of resistance in wheat breeding materials.

Authors

  • Hecang Zang
    Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou, China.
  • Congsheng Wang
    Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou, China.
  • Qing Zhao
    Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
  • Jie Zhang
    College of Physical Education and Health, Linyi University, Linyi, Shandong, China.
  • Junmei Wang
    Department of Pharmaceutical Sciences, Computational Chemical Genomics Screen Center, School of Pharmacy, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA, 15213, USA; Department of Pharmaceutical Sciences, School of Pharmacy, NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA, 15213, USA. Electronic address: junmei.wang@pitt.edu.
  • Guoqing Zheng
    Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou, China.
  • Guoqiang Li
    School of Electrical Engineering, Yanshan University, Qinhuangdao 066001, China.

Keywords

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