Estimation of wheat tiller density using remote sensing data and machine learning methods

文献类型： 外文期刊

第一作者： Hu, Jinkang

作者： Hu, Jinkang;Zhang, Bing;Peng, Dailiang;Yu, Ruyi;Ye, Huichun;Huang, Wenjiang;Cheng, Enhui;Yang, Songlin;Hu, Jinkang;Zhang, Bing;Cheng, Enhui;Yang, Songlin;Peng, Dailiang;Ye, Huichun;Huang, Wenjiang;Liu, Yao;Xiao, Chenchao;Li, Cunjun;Dong, Tao;Fang, Moren;Lin, Binbin;Wang, Mengmeng

作者机构：

关键词： winter wheat; tiller density; UAV hyperspectral; vegetation index; random forest; gradient boosted regression trees

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 tiller density is a key agronomic trait of winter wheat that is essential to field management and yield estimation. The traditional method of obtaining the wheat tiller density is based on manual counting, which is inefficient and error prone. In this study, we established machine learning models to estimate the wheat tiller density in the field using hyperspectral and multispectral remote sensing data. The results showed that the vegetation indices related to vegetation cover and leaf area index are more suitable for tiller density estimation. The optimal mean relative error for hyperspectral data was 5.46%, indicating that the results were more accurate than those for multispectral data, which had a mean relative error of 7.71%. The gradient boosted regression tree (GBRT) and random forest (RF) methods gave the best estimation accuracy when the number of samples was less than around 140 and greater than around 140, respectively. The results of this study support the extension of the tested methods to the large-scale monitoring of tiller density based on remote sensing data.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序