Grading detection of chili pepper diseases species and degree based on the SE-MultiResNet50 algorithm
In the actual pepper planting environment,due to its complex background,the identification of pepper leaf diseases has always been a challenging problem.Currently,there is a lack of publicly available datasets for severity grading detection and classification of chili pepper leaf diseases.This study focuses on leaf samples from the chili pepper plantation base of the Chengdu Academy of Agriculture and Forestry Sciences,by utilizing U2-Net for leaf segmentation to generate synthetic images with diverse complex backgrounds,thereby enriching the dataset.Addressing common chili pepper diseases such as bacterial spot,powdery mildew,viral diseases,and healthy leaves,a SE-MultiResNet50 detection model is proposed.This model performs remarkably well on a test set comprised entirely of images with complex backgrounds:achieving a recognition accuracy of 91.05％for chili pepper disease types and 92.08％for severity grading.Experimental results of this study demonstrate that the detection model exhibits high recognition accuracy under complex backgrounds,successfully achieving intelligent identification of chili pepper disease types and severity grading.Additionally,a novel dataset augmentation method is provided,offering new insights and avenues for research in related fields.
chilidiseases and pestshierarchical detectionattention mechanismResNet50
万方数据
维普
