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Detection of wheat scab fungus spores utilizing the Yolov5-ECA-ASFF network structure

文献类型： 外文期刊

作者： Zhang, Dong-Yan 1 ; Zhang, Wenhao 1 ; Cheng, Tao 1 ; Zhou, Xin-Gen 3 ; Yan, Zihao 2 ; Wu, Yuhang 2 ; Zhang, Gan 1 ; Yang, Xue 1 ;

作者机构： 1.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China

2.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applica, Hefei 230601, Peoples R China

3.Texas A&M AgriLife Res Ctr, Plant Pathol Lab, 1509 Aggie Dr, Beaumont, TX 77713 USA

4.Anhui Acad Agr Sci, Inst Plant Protect & Agroprod Safety, Hefei 230031, Peoples R China

关键词： Small target detection; Yolov5; Attention mechanism; Adaptive feature fusion; Fungal spore recognition

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2023 年 210 卷

页码：

收录情况： SCI

摘要： Rapid detection and identification of Fusarium germinate spores play a vital role in the early prediction and effective management of wheat scab disease. This study proposed an improved Yolov5-ECA-ASFF target detec-tion algorithm that addressed the challenges of small size and precise localization of spore image targets. The algorithm incorporated the attention mechanism module (ECA-Net) and adaptive feature fusion mechanism (ASFF) into the feature pyramid structure of YOLO, effectively tackling issues related to small size, limited characteristics, and unclear attributes of F. germinate spores. The results demonstrate that the proposed model achieved an average recognition accuracy of 98.57% for F. graminearum spores, surpassing the original Yolov5s algorithm's mAP value by 6.8%. The proposed method outperformed other mainstream target detection net-works like Yolov4 and Faster-RCNN. It also exhibited excellent recognition outcomes in scenarios involving multiple targets and complex backgrounds, while maintaining model robustness even when faced with similar appearance, morphology, and color characteristics of various scab spores. In conclusion, this method accurately detected and identified wheat scab spores in the presence of a variety of mixed spores, providing crucial technical support for automated detection of wheat scab spores and early prediction of wheat scab outbreaks under complex field environments.

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