基于改进YOLOv8s的小麦赤霉病害检测研究

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中图分类号：S126：S435.12 文献标识号：A 文章编号：1001-4942（2025）08-0149-11

Abstract To address the issue of low recognition accuracy of wheat scab lesions detected by existing models，which hindered subsequent prevention and control eforts，this study proposed a wheat scab lesion detection algorithm named YOLOv8s-SCR， which was an improved version based on the YOLOv8s model. Using YOLOv8s as the basic network，this algorithm incorporated the advantages of ShufleNetv2 and introduced the Squeeze and Excitation （SE）channel atention mechanism for enhancement，which could not only lightened the network model but also enhanced the model's focus on key features.Leveraging the adaptive training capability of the CARAFE（Content Aware ReAssembly of FEatures）module，the nearest-neighbor interpolation upsampling in the original YOLOv8s was replaced， which enabled the provision of richer semantic information during the upsampling process. A multi-scale and trainable RFB（Receptive Field Block） module was employed to further improve the model's detection performance by fusing features at diffrent scales.Experimental tests demonstrated that the YOLOv8s-SCR model reduced the number of network parameters by 21.02% and decreased FLOPS （FLoating-point Operations Per Second） by 24.48% compared to the original model. On the test set，the mean average precision （mAP） of the model increased from 84.6% in the original model to 90.5% ，representing 5.9 percentage points of improvement，thereby validated the effectiveness of the improved model in wheat scab detection.In summary，the YOLOv8s-SCR model proposed in this study could swiftly and effctively detect wheat scab lesions on wheat ears，providing robust support for subsequent prevention and control efforts.

KeywordsWheat scab； Disease identification; YOLOv8s； Network lightweighting; Upsampling； Fea-ture fusion

小麦是我国主要粮食作物之一，2022年我国小麦产量达到1.38亿吨，在全球小麦主产国中位居第一[1]，为我国应对各种风险挑战、稳定经济发展提供了有力支撑。（剩余13768字）