您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

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

文献类型： 外文期刊

作者： Li, Changchun 1 ; Wang, Yilin 1 ; Ma, Chunyan 1 ; Chen, Weinan 1 ; Li, Yacong 1 ; Li, Jingbo 1 ; Ding, Fan 1 ; Xiao, Zhen 1 ;

作者机构： 1.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454099, Henan, Peoples R China

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

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.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台