Prediction of severity grading of black measles disease in grapes based on improved BiSeNet
In order to accurately grade and predict the degree of black measles disease in grapes(Vitis vinifera L.),a semantic segmen-tation model was used to separate the leaf and lesion parts.The ratio of lesion area to total leaf area on the same leaf was used as the ba-sis for disease severity grading,and the degree of black measles disease in grapes was predicted.419 grapes disease images from the PlantVillage public database were accurately annotated and subdivided into three categories:background,leaves,and lesions,and data augmentation techniques were applied to increase sample diversity.Using BiSeNet as the benchmark model and introducing Ghost-Net as the backbone extraction network for context paths not only maintained a small number of model parameters,but also achieved a significant improvement in accuracy,meeting the needs of disease severity classification prediction.A cumulative atrous spatial pyra-mid pooling(CASPP)module was proposed to replace the single context embedding module in the BiSeNet model,in order to enhance the multi-scale context information extraction ability of the BiSeNet model and improve the segmentation accuracy of the model.After testing,the average Intersection over to Union of this research model in the test set was 94.11%.When predicting the degree of black measles disease in grapes,the accuracy reached 98.21%,which could accurately predict the degree of black measles disease in grapes.
万方数据
维普
