关键词: 黄瓜病害, 复杂背景, 语义分割, 两阶段框架, 病害严重程度分级

Abstract: In order to meet the demand for precise medication of diseased leaves in the actual planting environment, a complex background image of facility environment was taken as the research object, and a classification method of cucumber leaf disease under complex background based on semantic segmentation was proposed. First, Labelme labeling software was used to label the image leaves and disease spots, and image enhancement was performed on some diseased leaves to enrich the dataset; then, the UNet network structure was improved and a deep learningbased cucumber leaf disease segmentation under complex background was constructed. A twostage architecture is used to segment cucumber leaves and disease spots under complex backgrounds. Finally, the disease severity classification model DMUNet of cucumber downy mildew and anthracnose is proposed to classify the disease levels. The pixel accuracy, average intersection ratio and Dice coefficient of the improved UNet model are 90.48%, 92.46%, and 0.645 7, respectively, which are 2.36%, 2.34%, and 0.023 8 higher than the original model. The classification accuracy of cucumber downy mildew and anthracnose reaches 92.11% and 89.17%, respectively. The classification method of cucumber leaf disease severity based on semantic segmentation can achieve effective segmentation and classification of cucumber disease, and provide technical support for accurate disease prevention and control.

Key words: cucumber disease, complex background, semantic segmentation, twostage framework, disease severity classification