Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique

文献类型： 外文期刊

第一作者： Li, Changchun

作者： Li, Changchun;Wang, Yilin;Ma, Chunyan;Chen, Weinan;Li, Yacong;Li, Jingbo;Ding, Fan;Xiao, Zhen;Li, Jingbo

作者机构：

关键词： grain yield; hyperspectral vegetation index; leaf area index; elastic network; stacking technology

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.838； 五年影响因子：2.921 ）

ISSN：

年卷期： 2021 年 11 卷 24 期

页码：

收录情况： SCI

摘要： Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, anthesis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R-2 of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R-2 = 0.74), and the R-2 of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops.

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