Potato Leaf Disease Detection Algorithm Based on Improved YOLOv8
To address the issue of low detection accuracy of potato leaf diseases in complex background environments,a potato leaf dis-ease detection algorithm,named YOLOv8n-Potato,was proposed based on YOLOv8n.The algorithm replaces the neck network of YOLOv8 with the CAA-HS-FPN architecture to enhance feature fusion efficiency.Additionally,a lightweight detection head,Sc-Head,is used to replace the detection head of YOLOv8,making the model lightweight.Finally,PIoU is adopted to replace CIoU to reduce the cost of anchor box regression.Compared to YOLOv8n,YOLOv8n-Potato is 2.4%higher in accuracy,8.4%higher in recall rate,3.6%in mAP50,and 1%in mAP50-95,while GFLOPs reduced by 23%and model parameters by 42%.
万方数据
维普
