Identification Method of Feicheng Peach Diseases and Pests Based on Improved YOLOv7
In order to solve the difficulties in accurate identification of diseases and pests in Feicheng peach due to small size of features and morphological similarity of disease spots,the Feicheng peach planting base in Shandong Province was used as sample collection site,and a dataset was constructed containing six peach diseases and pests including bacterial perforation disease,brown spot perforation disease,hidden yello-wing disease,peach small heart borer,red necked beetle and gummosis.Based on distribution characteristics of samples,various methods such as Mixup,Cutout and Gaussian Blur were introduced for data augmentation to improve the model's detection accuracy of small disease spot features.Using YOLOv7 model as backbone network,Ghost module was added for slimming to reduce redundant features of the model,and multi-scale neural network model was constructed based on CBAM attention mechanism and weighted bidirectional feature pyramid network(BiFPN)to enhance multi-scale fusion of small disease spots and improve the generalization ability of the model.After experimental verification,the improved model achieved the mean average precision(mAP)of 93.2％for the six diseases and insects mentioned above,which indicated that it could identify pests and diseases effectively,and provide technical supports for early warning and prevention of diseases and pests in Feicheng peach.
Feicheng peachDisease and pest identificationYOLOv7 modelDeep learningConvolu-tional neural network
万方数据
维普
