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A Dynamic Detection Method for Phenotyping Pods in a Soybean Population Based on an Improved YOLO-v5 Network

文献类型： 外文期刊

作者： Fu, Xiaoming 1 ; Li, Aokang 1 ; Meng, Zhijun 3 ; Yin, Xiaohui 4 ; Zhang, Chi 1 ; Zhang, Wei 1 ; Qi, Liqiang 1 ;

作者机构： 1.Heilongjiang Bayi Agr Univ, Coll Engn, Daqing 163319, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Soybean Mechanized Prod, Daqing 163319, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

4.Daqing Oilfield Co Ltd, Oil Prod Plant Planning & Design Res Inst 4, Daqing 163511, Peoples R China

关键词： soybean pods; phenotyping; soybean population; dynamic detection; YOLO-v5; RGB-D

期刊名称：AGRONOMY-BASEL （ 影响因子：3.949； 五年影响因子：4.117 ）

ISSN：

年卷期： 2022 年 12 卷 12 期

页码：

收录情况： SCI

摘要： Pod phenotypic traits are closely related to grain yield and quality. Pod phenotype detection in soybean populations in natural environments is important to soybean breeding, cultivation, and field management. For an accurate pod phenotype description, a dynamic detection method is proposed based on an improved YOLO-v5 network. First, two varieties were taken as research objects. A self-developed field soybean three-dimensional color image acquisition vehicle was used to obtain RGB and depth images of soybean pods in the field. Second, the red-green-blue (RGB) and depth images were registered using an edge feature point alignment metric to accurately distinguish complex environmental backgrounds and establish a red-green-blue-depth (RGB-D) dataset for model training. Third, an improved feature pyramid network and path aggregation network (FPN+PAN) structure and a channel attention atrous spatial pyramid pooling (CA-ASPP) module were introduced to improve the dim and small pod target detection. Finally, a soybean pod quantity compensation model was established by analyzing the influence of the number of individual plants in the soybean population on the detection precision to statistically correct the predicted pod quantity. In the experimental phase, we analyzed the impact of different datasets on the model and the performance of different models on the same dataset under the same test conditions. The test results showed that compared with network models trained on the RGB dataset, the recall and precision of models trained on the RGB-D dataset increased by approximately 32% and 25%, respectively. Compared with YOLO-v5s, the precision of the improved YOLO-v5 increased by approximately 6%, reaching 88.14% precision for pod quantity detection with 200 plants in the soybean population. After model compensation, the mean relative errors between the predicted and actual pod quantities were 2% to 3% for the two soybean varieties. Thus, the proposed method can provide rapid and massive detection for pod phenotyping in soybean populations and a theoretical basis and technical knowledge for soybean breeding, scientific cultivation, and field management.

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