NIR attribute selection for the development of vineyard water status predictive models

Near-Infrared spectroscopy (NIR) returns full spectra in the region between 750 and 2500 nm. Although a full spectrum provides extremely informative data, sometimes this enormous amount of detail is redundant and does not bring any additional information. In this work, different attribute selection...

ver descrição completa

Detalhes bibliográficos
Autores: Marañón Grandes, Miguel, Fernández-Novales, Juan, Tardáguila, Javier, Gutiérrez, Salvador, Diago, Maria P.
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2023
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/337311
Acesso em linha:http://hdl.handle.net/10261/337311
Access Level:Acceso aberto
Palavra-chave:Grapevine
Stem water potential
Variable Importance in Projection scores
Manual wavelength selection
Interval Partial Least Squares
id ES_5bb26dc27072e7dcd3365e099396f405
oai_identifier_str oai:digital.csic.es:10261/337311
network_acronym_str ES
network_name_str España
repository_id_str
spelling NIR attribute selection for the development of vineyard water status predictive modelsMarañón Grandes, MiguelFernández-Novales, JuanTardáguila, JavierGutiérrez, SalvadorDiago, Maria P.GrapevineStem water potentialVariable Importance in Projection scoresManual wavelength selectionInterval Partial Least SquaresNear-Infrared spectroscopy (NIR) returns full spectra in the region between 750 and 2500 nm. Although a full spectrum provides extremely informative data, sometimes this enormous amount of detail is redundant and does not bring any additional information. In this work, different attribute selection methods for the development of vineyard water status predictive models are presented. Spectra from grapevine leaves were collected on-the-go (from a moving vehicle) along nine dates during the 2015 season in a commercial vineyard using a NIR spectrometer (1200–2100 nm). Contemporarily, the stem water potential (Ψ) was also measured in the monitored vines. A manual selection, based on Variable Importance in Projection scores (VIP scores) to choose the spectrum intervals including the most important wavelengths (interval selection), the locally most important wavelengths in the spectrum (peak selection), as well as the Interval Partial Least Squares (IPLS) were tested as attribute selection methods. The results obtained for the estimation of Ψ using the whole spectrum (R = 0.84, RMSEP = 0.167 MPa) were comparable to those yielded by the three attribute selection methods: the interval selection method (R = 0.80, RMSEP = 0.186 MPa), the peak selection method (R = 0.77, RMSEP = 0.201 MPa) and the IPLS (R ∼ 0.62–0.79, RMSEP ∼ 0.186–0.252 MPa). The highest simplification was provided by two IPLS models with three wavelengths and bandwidths of 20 and 4 nm that yielded R∼0.78 and RMSEP∼ 0.190 MPa. These results corroborate the suitability of a highly reduced selection of NIR wavelengths for the prediction of grapevine water status, and its utility to develop simpler multispectral devices for vineyard water status estimation.Grant PID2019-108330RA-I00 funded by MCIN/AEI/10.13039/501100011033 (Spain).Academic PressElsevierAgencia Estatal de Investigación (España)Ministerio de Ciencia, Innovación y Universidades (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2023202320232023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/337311reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108330RA-I00The underlying dataset has been published as supplementary material of the article in the publisher platform at http://dx.doi.org/10.1016/j.biosystemseng.2023.04.001http://dx.doi.org/10.1016/j.biosystemseng.2023.04.001Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3373112026-05-22T06:33:51Z
dc.title.none.fl_str_mv NIR attribute selection for the development of vineyard water status predictive models
title NIR attribute selection for the development of vineyard water status predictive models
spellingShingle NIR attribute selection for the development of vineyard water status predictive models
Marañón Grandes, Miguel
Grapevine
Stem water potential
Variable Importance in Projection scores
Manual wavelength selection
Interval Partial Least Squares
title_short NIR attribute selection for the development of vineyard water status predictive models
title_full NIR attribute selection for the development of vineyard water status predictive models
title_fullStr NIR attribute selection for the development of vineyard water status predictive models
title_full_unstemmed NIR attribute selection for the development of vineyard water status predictive models
title_sort NIR attribute selection for the development of vineyard water status predictive models
dc.creator.none.fl_str_mv Marañón Grandes, Miguel
Fernández-Novales, Juan
Tardáguila, Javier
Gutiérrez, Salvador
Diago, Maria P.
author Marañón Grandes, Miguel
author_facet Marañón Grandes, Miguel
Fernández-Novales, Juan
Tardáguila, Javier
Gutiérrez, Salvador
Diago, Maria P.
author_role author
author2 Fernández-Novales, Juan
Tardáguila, Javier
Gutiérrez, Salvador
Diago, Maria P.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Grapevine
Stem water potential
Variable Importance in Projection scores
Manual wavelength selection
Interval Partial Least Squares
topic Grapevine
Stem water potential
Variable Importance in Projection scores
Manual wavelength selection
Interval Partial Least Squares
description Near-Infrared spectroscopy (NIR) returns full spectra in the region between 750 and 2500 nm. Although a full spectrum provides extremely informative data, sometimes this enormous amount of detail is redundant and does not bring any additional information. In this work, different attribute selection methods for the development of vineyard water status predictive models are presented. Spectra from grapevine leaves were collected on-the-go (from a moving vehicle) along nine dates during the 2015 season in a commercial vineyard using a NIR spectrometer (1200–2100 nm). Contemporarily, the stem water potential (Ψ) was also measured in the monitored vines. A manual selection, based on Variable Importance in Projection scores (VIP scores) to choose the spectrum intervals including the most important wavelengths (interval selection), the locally most important wavelengths in the spectrum (peak selection), as well as the Interval Partial Least Squares (IPLS) were tested as attribute selection methods. The results obtained for the estimation of Ψ using the whole spectrum (R = 0.84, RMSEP = 0.167 MPa) were comparable to those yielded by the three attribute selection methods: the interval selection method (R = 0.80, RMSEP = 0.186 MPa), the peak selection method (R = 0.77, RMSEP = 0.201 MPa) and the IPLS (R ∼ 0.62–0.79, RMSEP ∼ 0.186–0.252 MPa). The highest simplification was provided by two IPLS models with three wavelengths and bandwidths of 20 and 4 nm that yielded R∼0.78 and RMSEP∼ 0.190 MPa. These results corroborate the suitability of a highly reduced selection of NIR wavelengths for the prediction of grapevine water status, and its utility to develop simpler multispectral devices for vineyard water status estimation.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/337311
url http://hdl.handle.net/10261/337311
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108330RA-I00
The underlying dataset has been published as supplementary material of the article in the publisher platform at http://dx.doi.org/10.1016/j.biosystemseng.2023.04.001
http://dx.doi.org/10.1016/j.biosystemseng.2023.04.001
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Academic Press
Elsevier
publisher.none.fl_str_mv Academic Press
Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869408821089140736
score 15,811543

Registros relacionados