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Title: Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

Abstract

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than the PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varietiesmore » in Miscanthus.« less

Authors:
 [1];  [1];  [2];  [3];  [4]
  1. Zhejiang Univ., Hangzhou (China)
  2. Kangwon National Univ., Chuncheon (South Korea)
  3. Hokkaido Univ., Sapporo (Japan)
  4. Univ. of Illinois, Urbana-Champaign, Urbana, IL (United States)
Publication Date:
Research Org.:
Univ. of Illinois, Urbana-Champaign, Urbana, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1389574
Grant/Contract Number:  
SC0006634; SC0012379
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Frontiers in Plant Science
Additional Journal Information:
Journal Volume: 8; Journal ID: ISSN 1664-462X
Publisher:
Frontiers Research Foundation
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Miscanthus; leaf water content; drought-resistant breeding; VIS/NIR spectroscopy; sensitive wavelengths

Citation Formats

Jin, Xiaoli, Shi, Chunhai, Yu, Chang Yeon, Yamada, Toshihiko, and Sacks, Erik J. Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus. United States: N. p., 2017. Web. doi:10.3389/fpls.2017.00721.
Jin, Xiaoli, Shi, Chunhai, Yu, Chang Yeon, Yamada, Toshihiko, & Sacks, Erik J. Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus. United States. doi:10.3389/fpls.2017.00721.
Jin, Xiaoli, Shi, Chunhai, Yu, Chang Yeon, Yamada, Toshihiko, and Sacks, Erik J. Fri . "Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus". United States. doi:10.3389/fpls.2017.00721. https://www.osti.gov/servlets/purl/1389574.
@article{osti_1389574,
title = {Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus},
author = {Jin, Xiaoli and Shi, Chunhai and Yu, Chang Yeon and Yamada, Toshihiko and Sacks, Erik J.},
abstractNote = {Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than the PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.},
doi = {10.3389/fpls.2017.00721},
journal = {Frontiers in Plant Science},
number = ,
volume = 8,
place = {United States},
year = {Fri May 19 00:00:00 EDT 2017},
month = {Fri May 19 00:00:00 EDT 2017}
}

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