山西农业大学学报（自然科学版）2025，Vol.45Issue(4)：78-87,10.DOI:10.13842/j.cnki.issn1671-8151.202412011
基于改进YOLOv8n的谷子谷瘟病检测方法
Foxtail millet blast detection method based on improved YOLOv8n
摘要
Abstract
[Objective]Intelligent detection of foxtail millet blast disease in field environments faces challenges such as strong background interference,varying lesion scales,and limited adaptability of existing models in real-world applications.[Methods]To address these issues,this study leveraged a large-scale foxtail millet blast dataset and proposed an improved detection mod-el,YOLOv8-SDL,based on YOLOv8n.[Results]The model enhanced detection stability and accuracy in complex back-grounds through three key modifications:strengthening backbone network feature extraction,introducing lightweight and effi-cient upsampling to improve feature fusion,and incorporating an attention mechanism to enhance critical feature selection.First,the Switchable Atrous Convolution(SAC)structure was optimized by adding a 3×3 convolution layer before the 1×1 convolution in its context structure.Combined with the Squeeze-and-Excitation(SE)channel attention mechanism,the SE-SAC module was constructed and embedded into the C2f module of the backbone network to enhance multi-scale feature extrac-tion of lesion areas.Second,DySample upsampling replaced nearest-neighbor interpolation.By employing a dynamic point-sampling strategy,DySample reduced computational overhead while minimizing feature information loss during upsampling,thereby improving feature fusion and small lesion localization accuracy.Third,the Large Separable Kernel Attention(LSKA)mechanism was integrated into the neck network.Its separable convolution design enhanced the model's ability to focus on dis-ease lesion features in complex backgrounds.Experimental results showed that YOLOv8-SDL achieved a detection accuracy of 91.5%,mAP@0.5 of 93.9%,and mAP@0.5～0.95 of 62.0%,outperforming the original model by 4.4%,1.4%,and 2.2%,respectively.[Conclusion]This model provided robust and reliable technical support for accurate foxtail millet blast de-tection in challenging field environments.关键词
深度学习/目标检测/谷瘟病/YOLOv8nKey words
Deep learning/Target detection/Foxtail millet blast/YOLOv8n分类
农业科技引用本文复制引用
翟正坤,张艺河,闫江林,韩渊怀,张宇波,成丽君..基于改进YOLOv8n的谷子谷瘟病检测方法[J].山西农业大学学报（自然科学版）,2025,45(4):78-87,10.基金项目
山西省基础研究计划青年科学研究项目(202303021222039) （202303021222039）
山西省教学改革创新项目(J20220247) （J20220247）
