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SE-SWIN UNET FOR IMAGE SEGMENTATION OF MAJOR MAIZE FOLIAR DISEASES

文献类型： 外文期刊

作者： Yang, Yujie 1 ; Wang, Congsheng 1 ; Zhao, Qing 3 ; Li, Guoqiang 3 ; Zang, Hecang 3 ;

作者机构： 1.Henan Normal Univ, Sch Comp & Informat Engn, Xinxiang 453007, Peoples R China

2.Key Lab Artificial Intelligence & Personalized Lea, Xinxiang 453000, Henan, Peoples R China

3.Henan Acad Agr Sci, Inst Agr Econ & Informat, Zhengzhou 450002, Peoples R China

4.Minist Agr & Rural Affairs, Huanghuaihai Key Lab Intelligent Agr Technol, Zhengzhou 450002, Peoples R China

关键词： maize leaf diseases; image segmentation; Swin-Unet; Swin transformer; SENet

期刊名称：ENGENHARIA AGRICOLA （ 影响因子：1.0； 五年影响因子：1.0 ）

ISSN： 0100-6916

年卷期： 2024 年 44 卷

页码：

收录情况： SCI

摘要： Maize yields are important for human food security, and the issue of how to quickly and accurately segment areas of maize disease is an important one in the field of smart agriculture. To address the problem of irregular and multi -area clustering of regions of maize leaf lesions, which can lead to inaccurate segmentation, this paper proposes an improved Swin-Unet model called squeeze -and -excitation Swin-Unet (SE-Swin Unet). Our model applies Swin Transformer modules and skip connection structures for global and local learning. At each skip connection, a SENet module is incorporated to focus on global target features through channel -wise attention, with the aims of highlighting significant regions of disease on maize leaves and suppressing irrelevant background areas. The improved loss function in SE-Swin Unet is based on a combination of the binary cross entropy and Dice loss functions, which form the semantic segmentation model. Compared to other traditional convolutional neural networks on the same dataset, SE-Swin Unet achieves higher mean results for the intersection over union, accuracy, and F1 -score, with values of 84.61%, 92.98%, and 89.91%, respectively. The SE-Swin Unet model proposed in this paper is therefore better able to extract information on maize leaf disease, and can provide a reference for the realisation of the complex task of corn leaf disease segmentation.

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