YOLOv5s-ECCW: A Lightweight Detection Model for Sugarcane Smut in Natural Environments

文献类型： 外文期刊

第一作者： Yu, Min

作者： Yu, Min;Li, Fengbing;Zhou, Xia;Zhu, Guanghu;Yang, Yunhai;Song, Xiupeng;Zhang, Xiaoqiu;Lei, Jingchao;Huang, Hairong;Huang, Dongmei;Li, Qiufang;Yan, Meixin;Song, Xiupeng;Zhang, Xiaoqiu;Lei, Jingchao;Huang, Hairong;Huang, Dongmei;Li, Qiufang;Yan, Meixin;Song, Xiupeng;Zhang, Xiaoqiu;Lei, Jingchao;Huang, Hairong;Huang, Dongmei;Li, Qiufang;Yan, Meixin;Wang, Zeping;Huang, Qiting;Fang, Hui;Huang, Weihua;Chen, Xiaohang

作者机构：

关键词： sugarcane disease; smut; YOLOv5s; lightweight model; attention mechanism

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 10 期

页码：

收录情况： SCI

摘要： Sugarcane smut, a serious disease caused by the fungus Sporosorium scitamineum, can result in 30% to 100% cane loss. The most affordable and efficient measure of preventing and handling sugarcane smut disease is to select disease-resistant varieties. A comprehensive evaluation of disease resistance based on the incidence of smut disease is essential during the selection process, necessitating the rapid and accurate identification of sugarcane smut. Traditional identification methods, which rely on visual observation of symptoms, are time-consuming, costly, and inefficient. To address these limitations, we present the lightweight sugarcane smut detection model (YOLOv5s-ECCW), which incorporates several innovative features. Specifically, the EfficientNetV2 is incorporated into the YOLOv5 network to achieve model compression while maintaining high detection accuracy. The convolutional block attention mechanism (CBAM) is added to the backbone network to improve its feature extraction capability and suppress irrelevant information. The C3STR module is used to replace the C3 module, enhancing the ability to capture global large targets. The WIoU loss function is used in place of the CIoU one to improve the bounding box regression's accuracy. The experimental results demonstrate that the YOLOv5s-ECCW model achieves a mean average precision (mAP) of 97.8% with only 4.9 G FLOPs and 3.25 M parameters. Compared with the original YOLOv5, our improvements include a 0.2% increase in mAP, a 54% reduction in parameters, and a 70.3% decrease in computational requirements. The proposed model outperforms YOLOv4, SSD, YOLOv5, and YOLOv8 in terms of accuracy, efficiency, and model size. The YOLOv5s-ECCW model meets the urgent need for the accurate real-time identification of sugarcane smut, supporting better disease management and selection of resistant varieties.

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