NDLI: Comparison of Four Chemometric Techniques for Estimating Leaf Nitrogen Concentrations in Winter Wheat (Triticum Aestivum) Based on Hyperspectral Features

Content Provider	Springer Nature Link
Author	Li, Zh. Nie, Ch. Wei, Ch. Xu, X. Song, X. Wang, J.
Copyright Year	2016
Abstract	Four chemometric techniques for estimating LNC in winter wheat were compared by spectral features. The predictive power and impact of sample size were evaluated. Key results include: (1) partial least squares regression (PLSR) and support vector machines regression (SVR) performed better than the other two methods, with coefficient of determination (r $^{2}$) values in the calibration set of 0.82 and 0.81 and the normalized root mean square error (NRMSE) values in the validation set of 5.48 and 5.94%, respectively; (2) the lowest accuracy was achieved using stepwise multiple linear regression (SMLR), with r $^{2}$ and NRMSE values of 0.78 and 6.52%, respectively; (3) the predictive power of the back propagation neural network (BPN) was enhanced as sample size increased. Sample size less than 80 is not recommended when using BPN. These results suggest that PLSR and SVR are preferred choices to estimate LNC in winter wheat, and BPN is recommended when a sufficient sample size is available.
Starting Page	240
Ending Page	247
Page Count	8
File Format	PDF
ISSN	00219037
Journal	Journal of Applied Spectroscopy
Volume Number	83
Issue Number	2
e-ISSN	15738647
Language	English
Publisher	Springer US
Publisher Date	2016-05-10
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	hyperspectral remote sensing stepwise multiple linear regression partial least squares regression artificial neural network support vector machines Atomic/Molecular Structure and Spectra Analytical Chemistry
Content Type	Text
Resource Type	Article
Subject	Spectroscopy Condensed Matter Physics

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