Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1017/wsc.2023.6 http://hdl.handle.net/11449/249694 |
Resumo: | The occurrence of weeds is one of the main factors limiting agricultural productivity. Studies on new techniques for the identification of these species can contribute to the development of proximal sensors, which in the future might be coupled to machines to optimize the performance of species-specific weed management. Thus, the objective of this study was to use near-infrared (NIR) spectroscopy and multivariate analysis to discriminate three morningglory species (Ipomoea spp.). The NIR spectra were collected from the leaves of the three weed species at the vegetative stage (up to five leaves), within the spectral band of 4,000 to 10,000 cm-1. The discrimination models were selected according to accuracy, sensitivity, specificity, and Youden's index and were analyzed with a validation data set (n = 135). The best results occurred when the selection of spectral bands associated with the use of preprocessing was performed. It was possible to obtain an accuracy of 99.3%, 98.5%, and 98.7% for ivyleaf morningglory (Ipomoea hederifolia L.), Japanese morningglory [Ipomoea nil (L.) Roth], and hairy woodrose [Merremia aegyptia (L.) Urb.], respectively. NIR spectroscopy associated with principal component analysis and linear discriminant analysis (PC-LDA) or partial least-squares regression with discriminant analysis (PLS-DA) can be used to discriminate Ipomoea spp. |
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spelling |
Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysisIpomoea hederifoliaIpomoea nilMerremia aegyptiaPC-LDAPLS-DAweed managementThe occurrence of weeds is one of the main factors limiting agricultural productivity. Studies on new techniques for the identification of these species can contribute to the development of proximal sensors, which in the future might be coupled to machines to optimize the performance of species-specific weed management. Thus, the objective of this study was to use near-infrared (NIR) spectroscopy and multivariate analysis to discriminate three morningglory species (Ipomoea spp.). The NIR spectra were collected from the leaves of the three weed species at the vegetative stage (up to five leaves), within the spectral band of 4,000 to 10,000 cm-1. The discrimination models were selected according to accuracy, sensitivity, specificity, and Youden's index and were analyzed with a validation data set (n = 135). The best results occurred when the selection of spectral bands associated with the use of preprocessing was performed. It was possible to obtain an accuracy of 99.3%, 98.5%, and 98.7% for ivyleaf morningglory (Ipomoea hederifolia L.), Japanese morningglory [Ipomoea nil (L.) Roth], and hairy woodrose [Merremia aegyptia (L.) Urb.], respectively. NIR spectroscopy associated with principal component analysis and linear discriminant analysis (PC-LDA) or partial least-squares regression with discriminant analysis (PLS-DA) can be used to discriminate Ipomoea spp.Support Foundation for the Technological Research Institute of the São Paulo State, SPICL Brasil, SPWeed Sciences Laboratory (LAPDA) Department of Biology Sao Paulo State University (Unesp/FCAV), SPDepartment of Agricultural Production Sao Paulo State University (Unesp/FCAV), SPDepartment of Horticulture Federal University of Goias, GODepartment of Biology Sao Paulo State University (Unesp/FCAV), SPWeed Sciences Laboratory (LAPDA) Department of Biology Sao Paulo State University (Unesp/FCAV), SPDepartment of Agricultural Production Sao Paulo State University (Unesp/FCAV), SPDepartment of Biology Sao Paulo State University (Unesp/FCAV), SPSupport Foundation for the Technological Research Institute of the São Paulo StateICL BrasilUniversidade Estadual Paulista (UNESP)Federal University of GoiasBraga, Andreísa FloresChiconi, Leandro AparecidoBacha, Allan Lopes [UNESP]Teixeira, Gustavo Henrique De Almeida [UNESP]Cunha Junior, Luis CarlosAlves, Pedro Luis Da Costa Aguiar [UNESP]2023-07-29T16:06:45Z2023-07-29T16:06:45Z2023-03-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article104-111http://dx.doi.org/10.1017/wsc.2023.6Weed Science, v. 71, n. 2, p. 104-111, 2023.1550-27590043-1745http://hdl.handle.net/11449/24969410.1017/wsc.2023.62-s2.0-85148892127Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengWeed Scienceinfo:eu-repo/semantics/openAccess2024-06-06T13:05:23Zoai:repositorio.unesp.br:11449/249694Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:30:00.484971Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis |
title |
Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis |
spellingShingle |
Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis Braga, Andreísa Flores Ipomoea hederifolia Ipomoea nil Merremia aegyptia PC-LDA PLS-DA weed management |
title_short |
Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis |
title_full |
Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis |
title_fullStr |
Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis |
title_full_unstemmed |
Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis |
title_sort |
Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis |
author |
Braga, Andreísa Flores |
author_facet |
Braga, Andreísa Flores Chiconi, Leandro Aparecido Bacha, Allan Lopes [UNESP] Teixeira, Gustavo Henrique De Almeida [UNESP] Cunha Junior, Luis Carlos Alves, Pedro Luis Da Costa Aguiar [UNESP] |
author_role |
author |
author2 |
Chiconi, Leandro Aparecido Bacha, Allan Lopes [UNESP] Teixeira, Gustavo Henrique De Almeida [UNESP] Cunha Junior, Luis Carlos Alves, Pedro Luis Da Costa Aguiar [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Support Foundation for the Technological Research Institute of the São Paulo State ICL Brasil Universidade Estadual Paulista (UNESP) Federal University of Goias |
dc.contributor.author.fl_str_mv |
Braga, Andreísa Flores Chiconi, Leandro Aparecido Bacha, Allan Lopes [UNESP] Teixeira, Gustavo Henrique De Almeida [UNESP] Cunha Junior, Luis Carlos Alves, Pedro Luis Da Costa Aguiar [UNESP] |
dc.subject.por.fl_str_mv |
Ipomoea hederifolia Ipomoea nil Merremia aegyptia PC-LDA PLS-DA weed management |
topic |
Ipomoea hederifolia Ipomoea nil Merremia aegyptia PC-LDA PLS-DA weed management |
description |
The occurrence of weeds is one of the main factors limiting agricultural productivity. Studies on new techniques for the identification of these species can contribute to the development of proximal sensors, which in the future might be coupled to machines to optimize the performance of species-specific weed management. Thus, the objective of this study was to use near-infrared (NIR) spectroscopy and multivariate analysis to discriminate three morningglory species (Ipomoea spp.). The NIR spectra were collected from the leaves of the three weed species at the vegetative stage (up to five leaves), within the spectral band of 4,000 to 10,000 cm-1. The discrimination models were selected according to accuracy, sensitivity, specificity, and Youden's index and were analyzed with a validation data set (n = 135). The best results occurred when the selection of spectral bands associated with the use of preprocessing was performed. It was possible to obtain an accuracy of 99.3%, 98.5%, and 98.7% for ivyleaf morningglory (Ipomoea hederifolia L.), Japanese morningglory [Ipomoea nil (L.) Roth], and hairy woodrose [Merremia aegyptia (L.) Urb.], respectively. NIR spectroscopy associated with principal component analysis and linear discriminant analysis (PC-LDA) or partial least-squares regression with discriminant analysis (PLS-DA) can be used to discriminate Ipomoea spp. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T16:06:45Z 2023-07-29T16:06:45Z 2023-03-15 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1017/wsc.2023.6 Weed Science, v. 71, n. 2, p. 104-111, 2023. 1550-2759 0043-1745 http://hdl.handle.net/11449/249694 10.1017/wsc.2023.6 2-s2.0-85148892127 |
url |
http://dx.doi.org/10.1017/wsc.2023.6 http://hdl.handle.net/11449/249694 |
identifier_str_mv |
Weed Science, v. 71, n. 2, p. 104-111, 2023. 1550-2759 0043-1745 10.1017/wsc.2023.6 2-s2.0-85148892127 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Weed Science |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
104-111 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129526787473408 |