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Estimation of soybean yield parameters under lodging conditions using RGB information from unmanned aerial vehicles

文献类型： 外文期刊

作者： Bai, Dong 1 ; Li, Delin 1 ; Zhao, Chaosen 2 ; Wang, Zixu 1 ; Shao, Mingchao 1 ; Guo, Bingfu 2 ; Liu, Yadong 1 ; Wang, Qi 1 ; Li, Jindong 1 ; Guo, Shiyu 1 ; Wang, Ruizhen 2 ; Li, Ying-hui 1 ; Qiu, Li-juan 1 ; Jin, Xiuliang 1 ;

作者机构： 1.Chinese Acad Agr Sci, Natl Key Facil Crop Gene Resources & Genet Improve, Key Lab Soybean Biol Beijing MOA, Inst Crop Sci,Key Lab Crop Gene Resource & Germpla, Beijing, Peoples R China

2.Jiangxi Acad Agr Sci, Nanchang Branch Natl Ctr Oil Crops Improvement, Jiangxi Prov Key Lab Oil Crops Biol, Crops Res Inst, Nanchang, Peoples R China

3.Northeast Agr Univ, Coll Agr, Harbin, Peoples R China

4.Chinese Acad Agr Sci, Natl Nanfan Res Inst Sanya, Sanya, Peoples R China

关键词： UAV; yield; soybean; lodging; machine learning

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： The estimation of yield parameters based on early data is helpful for agricultural policymakers and food security. Developments in unmanned aerial vehicle (UAV) platforms and sensor technology help to estimate yields efficiency. Previous studies have been based on less cultivars (<10) and ideal experimental environments, it is not available in practical production. Therefore, the objective of this study was to estimate the yield parameters of soybean (Glycine max (L.) Merr.) under lodging conditions using RGB information. In this study, 17 time point data throughout the soybean growing season in Nanchang, Jiangxi Province, China, were collected, and the vegetation index, texture information, canopy cover, and crop height were obtained by UAV-image processing. After that, partial least squares regression (PLSR), logistic regression (Logistic), random forest regression (RFR), support vector machine regression (SVM), and deep learning neural network (DNN) were used to estimate the yield parameters. The results can be summarized as follows: (1) The most suitable time point to estimate the yield was flowering stage (48 days), which was when most of the soybean cultivars flowered. (2) The multiple data fusion improved the accuracy of estimating the yield parameters, and the texture information has a high potential to contribute to the estimation of yields, and (3) The DNN model showed the best accuracy of training (R-2=0.66 rRMSE=32.62%) and validation (R-2=0.50, rRMSE=43.71%) datasets. In conclusion, these results provide insights into both best estimate period selection and early yield estimation under lodging condition when using remote sensing.

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