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Tytuł:
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Evaluation of different water absorption bands, indices and multivariate models for water-deficit stress monitoring in rice using visible-near infrared spectroscopy.
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Autorzy:
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Das B; Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India. Electronic address: .
Sahoo RN; Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India.
Pargal S; Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India.
Krishna G; Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India.
Verma R; Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India.
Viswanathan C; Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India.
Sehgal VK; Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India.
Gupta VK; Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India.
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Źródło:
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Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy [Spectrochim Acta A Mol Biomol Spectrosc] 2021 Feb 15; Vol. 247, pp. 119104. Date of Electronic Publication: 2020 Oct 24.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Publication: : Amsterdam : Elsevier
Original Publication: [Kidlington, Oxford, U.K. ; Tarrytown, NY] : Pergamon, c1994-
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MeSH Terms:
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Oryza*
Least-Squares Analysis ; Plant Leaves ; Spectroscopy, Near-Infrared ; Water
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Contributed Indexing:
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Keywords: Leaf water content; Multivariate models; Phenotyping; Spectral indices; VNIR spectroscopy; Water-deficit stress
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Substance Nomenclature:
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059QF0KO0R (Water)
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Entry Date(s):
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Date Created: 20201108 Date Completed: 20210514 Latest Revision: 20210514
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Update Code:
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20240105
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DOI:
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10.1016/j.saa.2020.119104
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PMID:
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33161273
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Accurate estimation of plant water status is a major factor in the decision-making process regarding general land use, crop water management and drought assessment. Visible-near infrared (VNIR) spectroscopy can provide an effective means for real-time and non-invasive monitoring of leaf water content (LWC) in crop plants. The current study aims to identify water absorption bands, indices and multivariate models for development of non-destructive water-deficit stress phenotyping protocols using VNIR spectroscopy and LWC estimated from 10 different rice genotypes. Existing spectral indices and band depths at water absorption regions were evaluated for LWC estimation. The developed models were found efficient in predicting LWC of the samples kept in the same environment with the ratio of performance to deviation (RPD) values varying from 1.49 to 3.05 and 1.66 to 2.63 for indices and band depths, respectively during validation. For identification of novel indices, ratio spectral indices (RSI) and normalised difference spectral indices (NDSI) were calculated in every possible band combination and correlated with LWC. The best spectral indices for estimating LWC of rice were RSI (R 1830 , R 1834 ) and NDSI (R 1830 , R 1834 ) with R 2 greater than 0.90 during training and validation, respectively. Among the multivariate models, partial least squares regression (PLSR) provided the best results for prediction of LWC (RPD = 6.33 and 4.06 for training and validation, respectively). The approach developed in this study will also be helpful for high-throughput water-deficit stress phenotyping of other crops.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2020 Elsevier B.V. All rights reserved.)
