东北林业大学学报2026，Vol.54Issue(6)：47-56,10.
基于改进 YOLOv10n 的植物叶片病害轻量化检测模型
A Lightweight Detection Model for Plant Leaf Diseases Based on Improved YOLOv10n:A Case Study of Solanum lycopersicum
摘要
Abstract
To address the problems of low efficiency in manual identification and insufficient intelligent monitoring methods for plant leaf diseases,Solanum lycopersicum was used as the experimental material,and a lightweight leaf disease detection algorithm DHS-YOLO based on the improved YOLOv10n network was proposed.First,the DCNv4 deformable convolution was introduced to enhance the model's feature perception ability and inference efficiency for irregular targets of leaf disea-ses.Second,the hierarchical hybrid attention module(C2f_HHA)was adopted to replace the original C2f structure,achieving accurate extraction of key disease features without increasing computational complexity.Finally,a novel spatial-channel-position integrated fusion module(SCPI)was designed in the neck network to strengthen the representation ability of multi-scale feature fusion for leaf diseases.To improve the generalization ability of the model,data augmentation opera-tions including flipping,mosaic,brightness adjustment and Gaussian noise addition were performed on the dataset.S.ly-copersicum leaf disease dataset containing 10 047 images was finally constructed and divided into training,validation and test sets with a ratio of 8:1:1.Experimental results showed that the mAP50 and mAP50-95 values of the DHS-YOLO model for S.lycopersicum leaf disease detection reached 94.7%and 79.9%,respectively,which were 4.1 and 4.0 per-centage points higher than those of the original YOLOv10n model.Meanwhile,the model size was reduced by 6.5%,and both precision and recall were synchronously optimized.关键词
植物病害检测/番茄/深度学习/图像检测/特征融合Key words
Plant disease detection/Solanum lycopersicum/Deep learning/Image detection/Feature fusion分类
信息技术与安全科学引用本文复制引用
朱莉,姜洪洋,黄建平..基于改进 YOLOv10n 的植物叶片病害轻量化检测模型[J].东北林业大学学报,2026,54(6):47-56,10.基金项目
国家自然科学基金项目(61701105). （61701105）
