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Wheat Fusarium head blight severity grading using generative adversarial networks and semi-supervised segmentation

文献类型： 外文期刊

作者： Feng, Guoqing 1 ; Gu, Ying 1 ; Wang, Cheng 1 ; Zhang, Dongyan 4 ; Xu, Rui 5 ; Zhu, Zhanwang 5 ; Luo, Bin 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100089, Peoples R China

2.Jiangsu Univ, Coll Agr Engn, Zhenjiang 712100, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

4.Northwest A&F Univ, Shaanxi Key Lab Agr Informat Percept & Intelligent, Yangling 212000, Shaanxi, Peoples R China

5.Hubei Acad Agr Sci, Food Crops Inst, Hubei Key Lab Food Crop Germplasm & Genet Improvem, Wuhan 430064, Hubei, Peoples R China

关键词： Wheat Fusarium head blight; Image generation; Super-resolution reconstruction; Knowledge distillation; Semantic segmentation

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 229 卷

页码：

收录情况： SCI

摘要： The severity of Fusarium head blight (FHB), a highly destructive disease of wheat spikes, can be graded using RGB images. To reduce the various costs required for image acquisition and the annotation costs required for segmentation models and to achieve accurate wheat FHB severity grading, this study proposed data augmentation strategies comprising StyleGAN3, Real-ESRGAN, and different input image resolutions, as well as the semi- supervised three-class segmentation model. StyleGAN3 and Real-ESRGAN, which use a generative adversarial network structure, were used in wheat spike image generation and super-resolution reconstruction in this study, respectively. High-quality generated images were screened based on their contribution to the FID scores for more reliable datasets. In addition, a semi-supervised segmentation network based on L-U2NetP and knowledge distillation was proposed, which reduced the annotation requirements by 60% while achieving three-class segmentation and severity grading of wheat spikes with FHB. This study also proposed the use of images of different resolutions at the input end and compared them with the proposed method. Results indicated that medium- resolution images could assist the model in achieving segmentation accuracy of 95.37% and grading accuracy of 96.88% while ensuring the integrity of the disease information. Compared with inputting high-resolution images, it can improve the transmission and super-resolution reconstruction rate on the application side. Meanwhile, high-resolution images also assisted the model in achieving segmentation accuracy of 95.75% and grading accuracy of 95.00%. The obtained models demonstrated strong feature extraction capabilities in heterogeneous test sets with complicated image backgrounds. Therefore, the proposed method can be used for image generation and application detection under different resource configurations and is a reliable and flexible tool for wheat FHB severity grading.

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