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Stacking of Canopy Spectral Reflectance from Multiple Growth Stages Improves Grain Yield Prediction under Full and Limited Irrigation in Wheat

文献类型： 外文期刊

作者： Hassan, Muhammad Adeel 1 ; Fei, Shuaipeng 2 ; Li, Lei 2 ; Jin, Yirong 1 ; Liu, Peng 1 ; Rasheed, Awais 2 ; Shawai, Rabiu Sani 2 ; Zhang, Liang 1 ; Ma, Aimin 1 ; Xiao, Yonggui 2 ; He, Zhonghu 2 ;

作者机构： 1.Dezhou Acad Agr Sci, Dezhou 253000, Peoples R China

2.Chinese Acad Agr Sci CAAS, Inst Crop Sci, Beijing 100081, Peoples R China

3.CAAS, Int Maize & Wheat Improvement Ctr CIMMYT China Of, Beijing 100081, Peoples R China

4.Quaid I Azam Univ, Dept Plant Sci, Islamabad 44000, Pakistan

关键词： bread wheat; phenotyping; vegetation indices; machine learning; stacking

期刊名称：REMOTE SENSING （ 影响因子：5.349； 五年影响因子：5.786 ）

ISSN：

年卷期： 2022 年 14 卷 17 期

页码：

收录情况： SCI

摘要： Grain yield (GY) prediction for wheat based on canopy spectral reflectance can improve selection efficiency in breeding programs. Time-series spectral information from different growth stages such as flowering to maturity is considered to have high accuracy in predicting GY and combining this information from multiple growth stages could effectively improve prediction accuracy. For this, 207 wheat cultivars and breeding lines were grown in full and limited irrigation treatments, and their canopy spectral reflectance was measured at the flowering, early, middle, and late grain fill stages. The potential of temporal spectral information at multiple growth stages for GY prediction was evaluated by a new method based on stacking the multiple growth stages data. Twenty VIs derived from spectral reflectance were used as the input feature of a support vector regression (SVR) to predict GY at each growth stage. The predicted GY values at multiple growth stages were trained by multiple linear regression (MLR) to establish a second-level prediction model. Results suggested that the prediction accuracy (R-2) of VIs data from single growth stages ranged from 0.60 to 0.66 and 0.35 to 0.42 in the full and limited irrigation treatments, respectively. The prediction accuracy was increased by an average of 0.06, 0.07, and 0.07 after stacking the VIs of two, three, and four growth stages, respectively, under full irrigation. Similarly, under limited irrigation, the prediction accuracy was increased by 0.03, 0.04, and 0.04 by stacking the VIs of two, three, and four growth stages, respectively. Stacking of VIs of multiple important growth stages can increase the accuracy of GY prediction and application of a stable stacking model could increase the usefulness of data obtained from different phenotyping platforms.

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