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Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing

文献类型： 外文期刊

作者： Zhang, Xiaoyan 1 ; Zhao, Jinming 1 ; Yang, Guijun 3 ; Liu, Jiangang 3 ; Cao, Jiqiu 1 ; Li, Chunyan 1 ; Zhao, Xiaoqing; 1 ;

作者机构： 1.Nanjing Agr Univ, Natl Key Lab Crop Genet & Germplasm Enhancement, MARA Key Lab Biol & Genet Improvement Soybean, Soybean Res Inst,Natl Ctr Soybean Improvement,Jia, Nanjing 210095, Peoples R China

2.Shandong Shofine Seed Technol Co Ltd, Jiaxiang 272400, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： soybean breeding; plot-yield prediction; UAV-based hyperspectral remote sensing; vegetation index; multiple linear regression; determination coefficient (R-2); root mean square error (RMSE)

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2019 年 11 卷 23 期

页码：

收录情况： SCI

摘要： Yield evaluation of breeding lines is the key to successful release of cultivars, which is becoming a serious issue due to soil heterogeneity in enlarged field tests. This study aimed at establishing plot-yield prediction models using unmanned aerial vehicle (UAV)-based hyperspectral remote sensing for yield-selection in large-scale soybean breeding programs. Three sets of soybean breeding lines (1103 in total) were tested in blocks-in-replication experiments for plot yield and canopy spectral reflectance on 454 similar to 950 nm bands at different growth stages using a UAV-based hyperspectral spectrometer (Cubert UHD185 Firefly). The four elements for plot-yield prediction model construction were studied respectively and concluded as: the suitable reflectance-sampling unit-size in a plot was its 20%-80% central part; normalized difference vegetation index (NDVI) and ration vegetation index (RVI) were the best combination of vegetation indices; the initial seed-filling stage (R5) was the best for a single stage prediction, while another was the best combination for a two growth-stage prediction; and multi-variate linear regression was suitable for plot-yield prediction. In model establishment for each material-set, a random half was used for modelling and another half for verification. Twenty-one two growth-stage two vegetation-index prediction models were established and compared for their modelling coefficient of determination (R-M(2)) and root mean square error of the model (RMSEM), verification R-V(2) and RMSEV, and their sum R-S(2) and RMSES. Integrated with the coincidence rate between the model predicted and the practical yield-selection results, the models, MA1-2, MA4-2 and MA6-2, with coincidence rates of 56.8%, 58.5% and 52.4%, respectively, were chosen for yield-prediction in yield-test nurseries. The established model construction elements and methods can be used as local models for pre-harvest yield-selection and post-harvest integrated yield-selection in advanced breeding nurseries as well as yield potential prediction in plant-derived-line nurseries. Furthermore, multiple models can be used jointly for plot-yield prediction in soybean breeding programs.

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