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

• Published in: Agronomy (Basel) Vol. 14; no. 10; p. 2327
• Main Authors: Yu, Min, Li, Fengbing, Song, Xiupeng, Zhou, Xia, Zhang, Xiaoqiu, Wang, Zeping, Lei, Jingchao, Huang, Qiting, Zhu, Guanghu, Huang, Weihua, Huang, Hairong, Chen, Xiaohang, Yang, Yunhai, Huang, Dongmei, Li, Qiufang, Fang, Hui, Yan, Meixin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Accuracy; Agricultural production; Algorithms; attention mechanism; Datasets; Deep learning; Disease; Disease resistance; Energy consumption; Identification methods; Lightweight; lightweight model; Modules; Natural environment; Neural networks; Object recognition; Parameters; Pornography; Real time; Smut; Sugarcane; sugarcane disease; Visual observation; Weight reduction; YOLOv5s; China
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy14102327

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.