农业与技术2026，Vol.46Issue(9)：47-51,5.DOI:10.19754/j.nyyjs.20260930009
基于改进YOLOv8的番茄叶片病害智能检测算法
Intelligent Tomato Leaf Disease Detection Algorithm Based on Improved YOLOv8
摘要
Abstract
To address the challenges of early identification of tomato leaf diseases and the large parameter size of conventional YOLOv8 models that hinder edge deployment,an intelligent tomato leaf disease detection method integrating Ghost lightweight convolution and the CBAM attention mechanism is proposed.Based on the YOLOv8n framework,all C2f modules in the backbone are replaced with C3Ghost,and downsampling convolutions are replaced with GhostConv to reduce computational cost while maintaining feature extraction capability.CBAM modules are further inserted after the SPPF module and before the three-scale outputs in the neck,using cascaded channel and spatial attention to enhance the model's focus on fine-grained lesion textures and small targets.Experiments on a public dataset containing 10,852 images covering 10 common tomato diseases demonstrate that the proposed Ghost-CBAM model achieves an mAP@0.5∶0.95 of 0.8762,surpassing the original YOLOv8n baseline by 0.94 percentage points and the CBAM-only variant by 0.25 percentage points.Meanwhile,the model parameter count is reduced to only 1.73×106(42.5％smaller than the baseline),with a weight file of just 3.7 MB.The proposed method effectively balances detection accuracy and model lightweighting,offering a practical solution for edge deployment in field settings.关键词
机番茄叶片病害/目标检测/YOLOv8/Ghost卷积/CBAM注意力/轻量化模型Key words
tomato leaf disease/object detection/YOLOv8/Ghost convolution/CBAM attention/lightweight model分类
农业科技引用本文复制引用
迟盛元,张晓洁,贾骐宁,孟凡松,赵艳芹..基于改进YOLOv8的番茄叶片病害智能检测算法[J].农业与技术,2026,46(9):47-51,5.基金项目
黑龙江省自然科学基金项目(项目编号:PL2025F048) （项目编号:PL2025F048）
哈尔滨信息工程学院校级自然科学基金项目(项目编号XZRJJ2026001) （项目编号XZRJJ2026001）
哈尔滨信息工程学院高年级课题(项目编号2526130103052,2526130103053) （项目编号2526130103052,2526130103053）
