Comparison of different machine learning algorithms for predicting maize grain yield using UAV-based hyperspectral images

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成果类型：

期刊论文

作者：

Guo, Yahui;Xiao, Yi;Hao, Fanghua;Zhang, Xuan;Chen, Jiahao;...

通讯作者：

Fu, YH

作者机构：

[Zhang, Xuan; Guo, Yahui; Fu, Yongshuo H.; Xiao, Yi] Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China.

[Hao, Fanghua; Chen, Jiahao] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.

[de Beurs, Kirsten] Wageningen Univ & Res, Lab Geoinformat Sci & Remote Sensing, POB 47, NL-6700 AA Wageningen, Netherlands.

[He, Yuhong] Univ Toronto, Dept Geog Geomatics & Environm, 3359 Mississauga Rd, Mississauga, ON L5L 1C6, Canada.

通讯机构：

[Fu, YH ] B

Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China.

语种：

英文

关键词：

Convolutional neural network (CNN);Full hyperspectral spectra;Machine learning (ML);Maize grain yield prediction;Multispectral and hyperspectral images

期刊：

International Journal of Applied Earth Observation and Geoinformation

ISSN：

1569-8432

年：

2023

卷：

124

DOI：

10.1016/j.jag.2023.103528

基金类别：

This study was funded by the joint fund for regional innovation and development of NSFC (Grant No. U21A2039), the National Funds for Distinguished Young Youths (Grant No. 42025101) and the 111 Project (Grant No. B18006).We thank deeply the AZUP Scientific (www.azup.cn) for helping with the hyperspectral image collection.

机构署名：

本校为其他机构

院系归属：

城市与环境科学学院

摘要：

Timely and accurately predicting maize grain yields will contribute to making adaptive measures to improve management practice and to adjust consumption patterns for ensuring food security. Unmanned aerial vehicles (UAV) are widely used to obtain high-temporal and high-spatial resolution remote sensing images of crops, enabling a possible sensor performance comparison. To date, few studies have compared the potential abilities of multispectral-based and hyperspectral-based images, only sensitive spectral wavelength and full hyperspectral spectr...

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Comparison of different machine learning algorithms for predicting maize grain yield using UAV-based hyperspectral images

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