基于改进YOLOv8n的海棠叶片病害检测方法OA

Aiming at the difficulties in the identification of ornamental plant pests and diseases,taking crabapple trees as example,which are common ornamental species in northern China,this paper proposes a detection method for common leaf diseases based on the improved YOLOv8n(MCSW-YOLOv8).This method targets crabapple leaves in natural environments,and enhances the collected samples.The following optimizations are applied to the model:Replace the backbone network of the original model with MobileNetV4,adopt Wise-IoU(WIoU)V3 as the loss function for bounding box regression,and integrate SPP and ELAN to enhance the model's capability to recognize objects of varying scales.Finally,incorporate CA attention mechanism to decompose horizontal and vertical pooling,retain position information,and improve the accuracy of boundary frame positioning in target detection.The results show that the MCSW-YOLOv8 object detection algorithm proposed in this paper improves the precision by 7.32%,mAP@0.5 by 7.03%,and mAP@0.5:0.95 by 3.53%on the dataset,achieving satisfactory detection performance.