山东农业大学学报（自然科学版）2026，Vol.57Issue(2)：295-305,11.DOI:10.3969/j.issn.1000-2324.2026.02.010
基于改进YOLOv8n的海棠叶片病害检测方法
Detection Algorithm for Crabapple Leaf Diseases Based on Improved YOLOv8n
郭秀梅 1杨存志 1王硕 1丛晓燕 1孙波1
作者信息
- 1. 山东农业大学信息科学与工程学院,山东 泰安 271018
- 折叠
摘要
Abstract
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.关键词
深度学习/YOLOv8n/目标检测/海棠叶片病害Key words
Deep learning/YOLOv8n/object detection/crabapple leaf diseases分类
信息技术与安全科学引用本文复制引用
郭秀梅,杨存志,王硕,丛晓燕,孙波..基于改进YOLOv8n的海棠叶片病害检测方法[J].山东农业大学学报（自然科学版）,2026,57(2):295-305,11.
