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Assessment of the levels of damage caused by Fusarium head blight in wheat using an improved YoloV5 method

文献类型： 外文期刊

作者： Zhang, Dong-Yan 1 ; Luo, Han-Sen 1 ; Wang, Dao-Yong 1 ; Zhou, Xin-Gen 1 ; Li, Wei-Feng 1 ; Gu, Chun-Yan 1 ; Zhang, Gan 1 ; He, Fang-Ming 1 ;

作者机构： 1.Anhui Univ, Natl Engn Res Ctr Agro Ecol Big Data Anal & Applic, Hefei 230601, Peoples R China

2.SiChuan Univ, Coll Elect & Informat Engn, Chengdu 610065, Peoples R China

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

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

关键词： Wheat; Fusarium head blight; Wheat ear counting; YoloV5; Image segmentation; DIOU

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 198 卷

页码：

收录情况： SCI

摘要： Object segmentation in deep learning has been recently used for the detection of Fusarium head blight (FHB), a worldwide disease in wheat. Such method, however, cannot detect the disease with high accuracy and is difficult to be used in labelling annotation. However, object detection network can solve the above problem. The object detection network has high detection accuracy and easy for labeling. Yolov5 is an advanced object detection network, but it can't detect the neighboring wheat ears well. So in this study, a novel method was developed based on object detection network, feature extraction and classifier to overcome these disadvantages. We combined Yolov5 object detection network with distance intersection over union non maximum suppression (DIOU-NMS) to form an improved Yolov5 object detection network. The improved YoloV5 object detection network was employed to detect and record wheat ears in images collected from field plots at two locations over 2 years. Pre-segmentation was conducted for single individual wheat ear images using threshold segmentation; HSV and CMYK color spaces were used as the baseline in each wheat ear image for extracting comprehensive color feature (CCF). The Res-Net network was used for extracting each wheat ear's high dimension feature (HDF). CCF and HDF were then merged as the comprehensive feature (CF) of each single wheat image. The random forest was used to classify wheat ear images into healthy wheat ears and diseased wheat ears by CF and then calculate the ratio of diseased wheat ears to total wheat ears as the level of damage caused by FHB. The results of performance evaluation of the proposed method in two different locations and years demonstrate its strong robustness in both time and spatial domains to effectively detect the levels of damage caused by FHB under the complex field background conditions. The average detection accuracy and detection time were 90.67% and 0.73 ms, respectively. The average accuracies of counting total wheat ears and diseased wheat ears were 96.16% and 81.66%, respectively. The improved YoloV5 method developed from this study can be used as a quick, efficient, and convenient tool for assessment of the levels of damage caused by FHB in wheat under field conditions.

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