NDLI: Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods

Content Provider	Springer Nature : BioMed Central
Author	Zhang, Juanjuan Cheng, Tao Guo, Wei Xu, Xin Qiao, Hongbo Xie, Yimin Ma, Xinming
Abstract	Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.
Related Links	https://plantmethods.biomedcentral.com/counter/pdf/10.1186/s13007-021-00750-5.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	17464811
DOI	10.1186/s13007-021-00750-5
Journal	Plant Methods
Issue Number	1
Volume Number	17
Language	English
Publisher	BioMed Central
Publisher Date	2021-05-03
Access Restriction	Open
Subject Keyword	Plant Sciences Biological Techniques Winter wheat Leaf area index Unmanned aerial vehicle Hyperspectral imaging data Characteristic bands Machine learning Model
Content Type	Text
Resource Type	Article
Subject	Plant Science Biotechnology Genetics
Journal Impact Factor	4.7/2023
5-Year Journal Impact Factor	5.6/2023

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