Identification method of crop diseases and insect pests based on improved YOLOv5n model
In order to solve the problems of low recognition accuracy for crop diseases and insect pests in complex scenes and large model parameters of the model,the lightweight YOLOv5n model was improved in this study.Firstly,a co-ordinate attention module was added to the backbone network of YOLOv5n model to make the model focus on the detection target and its location and reduce the influence of complex background on the model.Secondly,the weighted bi-directional feature fusion pyramid network(BiFPN)was introduced to reduce the information loss of small targets and improve the model's feature learning ability.Finally,the loss function SIoU was used to replace the loss function CIoU,which improved the target detection accuracy without changing the parameters of the model.In the dataset of corn pests and diseases collect-ed by unmanned air vehicle,the AgriPest-YOLOv5n model mAP@0.50 proposed by this study reached 81.32%,and the detection speed reached 77 FPS on the Jetson Xavier development board.The size of the model was 1.63 MB.The improved YOLOv5n model can meet the requirement of light weight,and can identify crop diseases and insect pests in real time and accurately under complex background.The results of this study provide technical support for the precision control of crop diseases and insect pests.
agricultural pest and diseaseobject detectionlightweight modelattention mechanism
万方数据
维普
