基于改进YOLOv8n的真实场景下草莓病害检测方法  

A method for strawberry disease detection in real scenarios based on improved YOLOv8n

作　　者：李嘉诚 陈中举[1] 许浩然 Li Jiacheng;Chen Zhongju;Xu Haoran(School of Computer Science,Yangtze University,Jingzhou,434023,China)

机构地区：[1]长江大学计算机科学学院,湖北荆州434023

出　　处：《中国农机化学报》2024年第12期267-274,共8页Journal of Chinese Agricultural Mechanization

基　　金：湖北省教育厅科学技术研究项目(B2021052);中国高校产学研创新基金—新一代信息技术创新项目(2023IT269)。

摘　　要：针对实际种植环境下草莓病害目标检测中,存在背景复杂、检测精度低等主要问题,提出一种改进YOLOv8n的草莓病害检测算法YOLOv8n-SD。搜集并处理真实场景下草莓叶、花、果的常见病害图像以构建试验数据集。在YOLOv8n模型的基础上对其进行优化改进,利用多尺度并行计算与补丁感知注意力对主卷积模块进行重构,提出C2f-PPA模块,有效融合多尺度特征信息,提高模型的特征捕获能力。引入ADown模块,减少下采样过程中的信息损失,提高模型的推理速度和鲁棒性。提出一种任务对齐的共享动态检测头(Task-aligned Dynamic Head, TDyH),增强定位分支和分类分支之间的信息交互,降低模型参数的同时,提高检测精度和准确性。根据试验结果,改进后的YOLOv8n-SD模型的检测精度达到83.7%,相较于原YOLOv8n提高3.3%,mAP@0.5与mAP@0.5∶0.95分别达到76.9%和59.9%,分别提升1.6%和2.3%。改进后的算法能精确识别草莓生长各阶段的常见病害,并满足边缘设备的轻量化需求和实时检测需求。Addressing the primary issues of complex backgrounds and low detection accuracy in strawberry disease target detection under practical farming conditions,an improved YOLOv8n strawberry disease detection algorithm as YOLOV8N-SD is proposed.Images of common strawberry leaf,flower,and fruit diseases under real-world scenarios are collected and processed to construct an experimental dataset.Optimizations and improvements are made to the YOLOv8n model.The primary convolutional module is reconstructed by using multi-scale parallel computing and patch-perceptive attention,introducing the C2f-PPA module.This effectively integrates multi-scale feature information,enhancing the model s feature capturing capability.Additionally,the ADown module is incorporated to reduce information loss during downsampling,thereby improving the model s inference speed and robustness.A Task-aligned Dynamic Head(TDyH)is proposed to strengthen information exchange between the localization and classification branches.This reduces model parameters while simultaneously enhancing detection precision and accuracy.According to experimental results,the improved YOLOv8n-SD model achieves a detection accuracy of 83.7%,representing a 3.3%increase over the original YOLOv8n.Its mAP@0.5 and mAP@0.5∶0.95 scores reach 76.9%and 59.9%respectively,marking improvements of 1.6%and 2.3%compared to the baseline.This enhanced algorithm not only accurately identifies common diseases at various stages of strawberry growth but also meets the lightweight and real-time detection requirements of edge devices.

关 键 词：草莓病害 目标检测 YOLOv8n ADown 轻量化 实时检测 

分 类 号：S436.68[农业科学—农业昆虫与害虫防治] TP391.4[农业科学—植物保护]