Estimation of wheat tiller density using remote sensing data and machine learning methods
文献类型： 外文期刊
作者： Hu, Jinkang 1 ; Zhang, Bing 1 ; Peng, Dailiang 1 ; Yu, Ruyi 1 ; Liu, Yao 4 ; Xiao, Chenchao 4 ; Li, Cunjun 5 ; Dong, Tao 6 ; Fang, Moren 7 ; Ye, Huichun 1 ; Huang, Wenjiang 1 ; Lin, Binbin 8 ; Wang, Mengmeng 9 ; Cheng, Enhui 1 ; Yang, Songlin 1 ;
作者机构： 1.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing, Peoples R China
3.Int Res Ctr Big Data Sustainable Dev Goals, Beijing, Peoples R China
4.Minist Nat Resources China, Land Satellite Remote Sensing Applicat Ctr, Beijing, Peoples R China
5.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing, Peoples R China
6.Aerosp ShuWei High Tech Co Ltd, Beijing, Peoples R China
7.Beijing Azup Sci Co Ltd, Beijing, Peoples R China
8.Texas A&M Univ, Dept Geog, College Stn, TX USA
9.China Univ Geosci Wuhan, Sch Geog & Informat Engn, Wuhan, Peoples R China
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.
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