Improved Pathologic Detection Algorithm of Apple Leaves Based on YOLOv7
China is a traditional agricultural country,The advanced pathological detection method of apple leaves can effectively reduce the pesticide spraying of forest land,reduce the economic burden of farmers,and play an important role in protecting the environment and reducing soil pollution.However,many current detection models of pathological insect infestation on apple leaves are unable to accurately,timely and effectively detect different insect surfaces,which hinders the development of machine learning modules on forest crops such as apples.This paper adds convolutional attention mechanism module(CBAM)and lightweight model(MobileNet)based on YOLOv7,so a new detection model Pest-net is established,CBAM attention mechanism is adopted,taking into account space and channel,to improve the defect detection accuracy of apple leaves in all directions,but at the same time,redundant parameters will be added and more computing resources will be required.Therefore,combined with MobileNet light-weight model,reduce the amount of calculation,improve the speed of detection.Finally,SIOU loss function is intro-duced to improve the robustness of the model.The experimental results show that compared with other four common target detection models,Pest-net have achieved higher accuracy rates on mAP(97.1％)and precision(94.5％).Therefore,Pest-net studied in this thesis have better application prospects in the pathological detection of apple leaves.
apple leaf diseasetarget detectionloss functionattention mechanismYOLOv7
NETL
NSTL
万方数据
维普
