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Enhancing wheat Fusarium head blight detection using rotation Yolo wheat detection network and simple spatial attention network

文献类型： 外文期刊

作者： Zhang, Dong-Yan 1 ; Luo, Han-Sen 1 ; Cheng, Tao 1 ; Li, Wei-Feng 1 ; Zhou, Xin-Gen 3 ; Wei-Guo 4 ; Gu, Chun-Yan 5 ; Diao, Zhihua 6 ;

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

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

3.Texas A&M Univ, Texas A&M AgriLife Res Ctr, Beaumont, TX 77713 USA

4.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou 450002, Peoples R China

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

6.Zhengzhou Univ Light Ind, Sch Elect Informat Engn, Zhengzhou 450002, Peoples R China

关键词： Wheat; Fusarium head blight; Fusarium graminearum; Rotation detection; Yolo; Unsupervised segmentation; Gray encoding

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 211 卷

页码：

收录情况： SCI

摘要： The detection of Fusarium head blight (FHB), a destructive disease in wheat, can be performed through digit imaging. To improve detection accuracy and overcome challenges related to accurate labelling and detection efficacy, this study introduced two new networks: the Rotation Yolo Wheat Detection (RYWD) network and the Simple Spatial Attention (SSA) network. The RYWD network, utilizing the Yolo structure, served as a novel rotation detector capable of detecting wheat head images with detection boxes of arbitrary orientations. Angle prediction performance was enhanced by employing gray coding labels for angle encoding. Additionally, the SSA network, an unsupervised segmentation network, incorporated a spatial attention module and a spatial conti-nuity loss to extract wheat features based on their spatial distribution. FHB detection was accomplished through HSV threshold segmentation and K-Means segmentation. The proposed method achieved an average accuracy of 94.66% in predicting the levels of FHB across two different years and locations. Comparatively, the proposed method outperformed previous research, exhibiting significant increases in both accuracy (11.8% increase) and precision (10.7% increase). These findings highlight the considerable improvement attainable through the integration of a rotation detector in crop disease detection, demonstrating its enhanced efficiency.

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