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A Cucumber Leaf Disease Severity Grading Method in Natural Environment Based on the Fusion of TRNet and U-Net

文献类型： 外文期刊

作者： Yao, Hui 1 ; Wang, Chunshan 1 ; Zhang, Lijie 4 ; Li, Jiuxi 4 ; Liu, Bo 1 ; Liang, Fangfang 1 ;

作者机构： 1.Hebei Agr Univ, Sch Informat Sci & Technol, Baoding 071001, Peoples R China

2.Hebei Key Lab Agr Big Data, Baoding 071001, Peoples R China

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

4.Hebei Agr Univ, Coll Mech & Elect Engn, Baoding 071001, Peoples R China

关键词： cucumber disease; disease spot; fusion of TRNet and U-Net; two-stage segmentation framework; disease severity grading

期刊名称：AGRONOMY-BASEL （ 影响因子：3.3； 五年影响因子：3.7 ）

ISSN：

年卷期： 2024 年 14 卷 1 期

页码：

收录情况： SCI

摘要： Disease severity grading is the primary decision-making basis for the amount of pesticide usage in vegetable disease prevention and control. Based on deep learning, this paper proposed an integrated framework, which automatically segments the target leaf and disease spots in cucumber images using different semantic segmentation networks and then calculates the area of disease spots and the target leaf for disease severity grading. Two independent datasets of leaves and lesions were constructed, which served as the training set for the first-stage diseased leaf segmentation and the second-stage lesion segmentation models. The leaf dataset contains 1140 images, and the lesion data set contains 405 images. The proposed TRNet was composed of a convolutional network and a Transformer network and achieved an accuracy of 93.94% by fusing local features and global features for leaf segmentation. In the second stage, U-Net (Resnet50 as the feature network) was used for lesion segmentation, and a Dice coefficient of 68.14% was obtained. After integrating TRNet and U-Net, a Dice coefficient of 68.83% was obtained. Overall, the two-stage segmentation network achieved an average accuracy of 94.49% and 94.43% in the severity grading of cucumber downy mildew and cucumber anthracnose, respectively. Compared with DUNet and BLSNet, the average accuracy of TUNet in cucumber downy mildew and cucumber anthracnose severity classification increased by 4.71% and 8.08%, respectively. The proposed model showed a strong capability in segmenting cucumber leaves and disease spots at the pixel level, providing a feasible method for evaluating the severity of cucumber downy mildew and anthracnose.

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