Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration
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/10.1007/s11676-022-01557-3 http://hdl.handle.net/11449/247855 |
Resumo: | Nitrogen (N) monitoring is essential in nurseries to ensure the production of high-quality seedlings. Near-infrared spectroscopy (NIRS) is an instantaneous, nondestructive method to monitor N. Spectral data such as NIRS can also provide the basis for developing a new vegetation spectral index (VSI). Here, we evaluated whether NIRS combined with statistical modeling can accurately detect early variations in N concentration in leaves of young plants of Annona emarginata and developed a new VSI for this task. Plants were grown in a hydroponics system with 0, 2.75, 5.5 or 11 mM N for 45 days. Then we measured gas exchange, chlorophylla fluorescence, and pigments in leaves; analyzed complete leaf nutrients, and recorded spectral data for leaves at 966 to 1685 nm using NIRS. With a statistical learning approach, the dimensionality of the spectral data was reduced, then models were generated using two classes (N deficiency, N) or four classes (0, 2.75, 5.5, 11 mM N). The best combination of techniques for dimensionality reduction and classification, respectively, was stepwise regression (PROC STEPDISC) and linear discriminant function. It was possible to detect N deficiency in seedlings leaves with 100% precision, and the four N concentrations with 93.55% accuracy before photosynthetic damage to the plant occurred. Thereby, NIRS combined with statistical modeling of multidimensional data is effective for detecting N variations in seedlings leaves of A. emarginata. |
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Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentrationDigital signatureFluorescence of chlorophyllaMineral nutrition of plantsNear-infrared spectroscopySpectral vegetation indexStatistical learningNitrogen (N) monitoring is essential in nurseries to ensure the production of high-quality seedlings. Near-infrared spectroscopy (NIRS) is an instantaneous, nondestructive method to monitor N. Spectral data such as NIRS can also provide the basis for developing a new vegetation spectral index (VSI). Here, we evaluated whether NIRS combined with statistical modeling can accurately detect early variations in N concentration in leaves of young plants of Annona emarginata and developed a new VSI for this task. Plants were grown in a hydroponics system with 0, 2.75, 5.5 or 11 mM N for 45 days. Then we measured gas exchange, chlorophylla fluorescence, and pigments in leaves; analyzed complete leaf nutrients, and recorded spectral data for leaves at 966 to 1685 nm using NIRS. With a statistical learning approach, the dimensionality of the spectral data was reduced, then models were generated using two classes (N deficiency, N) or four classes (0, 2.75, 5.5, 11 mM N). The best combination of techniques for dimensionality reduction and classification, respectively, was stepwise regression (PROC STEPDISC) and linear discriminant function. It was possible to detect N deficiency in seedlings leaves with 100% precision, and the four N concentrations with 93.55% accuracy before photosynthetic damage to the plant occurred. Thereby, NIRS combined with statistical modeling of multidimensional data is effective for detecting N variations in seedlings leaves of A. emarginata.Bioestatistic Plant Biology Parasitology and Zoology Department Bioscience Institute (IBB) Universidade Estadual Paulista (UNESP), São PauloBioestatistic Plant Biology Parasitology and Zoology Department Bioscience Institute (IBB) Universidade Estadual Paulista (UNESP), São PauloUniversidade Estadual Paulista (UNESP)Gomes, Rafaela Lanças [UNESP]Sousa, Marília Caixeta [UNESP]Campos, Felipe Girotto [UNESP]Boaro, Carmen Sílvia Fernandes [UNESP]de Souza Passos, José Raimundo [UNESP]Ferreira, Gisela [UNESP]2023-07-29T13:27:46Z2023-07-29T13:27:46Z2023-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article269-282http://dx.doi.org/10.1007/s11676-022-01557-3Journal of Forestry Research, v. 34, n. 1, p. 269-282, 2023.1993-06071007-662Xhttp://hdl.handle.net/11449/24785510.1007/s11676-022-01557-32-s2.0-85141680342Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Forestry Researchinfo:eu-repo/semantics/openAccess2023-07-29T13:27:46Zoai:repositorio.unesp.br:11449/247855Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:12:01.681627Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration |
title |
Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration |
spellingShingle |
Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration Gomes, Rafaela Lanças [UNESP] Digital signature Fluorescence of chlorophylla Mineral nutrition of plants Near-infrared spectroscopy Spectral vegetation index Statistical learning |
title_short |
Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration |
title_full |
Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration |
title_fullStr |
Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration |
title_full_unstemmed |
Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration |
title_sort |
Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration |
author |
Gomes, Rafaela Lanças [UNESP] |
author_facet |
Gomes, Rafaela Lanças [UNESP] Sousa, Marília Caixeta [UNESP] Campos, Felipe Girotto [UNESP] Boaro, Carmen Sílvia Fernandes [UNESP] de Souza Passos, José Raimundo [UNESP] Ferreira, Gisela [UNESP] |
author_role |
author |
author2 |
Sousa, Marília Caixeta [UNESP] Campos, Felipe Girotto [UNESP] Boaro, Carmen Sílvia Fernandes [UNESP] de Souza Passos, José Raimundo [UNESP] Ferreira, Gisela [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Gomes, Rafaela Lanças [UNESP] Sousa, Marília Caixeta [UNESP] Campos, Felipe Girotto [UNESP] Boaro, Carmen Sílvia Fernandes [UNESP] de Souza Passos, José Raimundo [UNESP] Ferreira, Gisela [UNESP] |
dc.subject.por.fl_str_mv |
Digital signature Fluorescence of chlorophylla Mineral nutrition of plants Near-infrared spectroscopy Spectral vegetation index Statistical learning |
topic |
Digital signature Fluorescence of chlorophylla Mineral nutrition of plants Near-infrared spectroscopy Spectral vegetation index Statistical learning |
description |
Nitrogen (N) monitoring is essential in nurseries to ensure the production of high-quality seedlings. Near-infrared spectroscopy (NIRS) is an instantaneous, nondestructive method to monitor N. Spectral data such as NIRS can also provide the basis for developing a new vegetation spectral index (VSI). Here, we evaluated whether NIRS combined with statistical modeling can accurately detect early variations in N concentration in leaves of young plants of Annona emarginata and developed a new VSI for this task. Plants were grown in a hydroponics system with 0, 2.75, 5.5 or 11 mM N for 45 days. Then we measured gas exchange, chlorophylla fluorescence, and pigments in leaves; analyzed complete leaf nutrients, and recorded spectral data for leaves at 966 to 1685 nm using NIRS. With a statistical learning approach, the dimensionality of the spectral data was reduced, then models were generated using two classes (N deficiency, N) or four classes (0, 2.75, 5.5, 11 mM N). The best combination of techniques for dimensionality reduction and classification, respectively, was stepwise regression (PROC STEPDISC) and linear discriminant function. It was possible to detect N deficiency in seedlings leaves with 100% precision, and the four N concentrations with 93.55% accuracy before photosynthetic damage to the plant occurred. Thereby, NIRS combined with statistical modeling of multidimensional data is effective for detecting N variations in seedlings leaves of A. emarginata. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T13:27:46Z 2023-07-29T13:27:46Z 2023-02-01 |
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/10.1007/s11676-022-01557-3 Journal of Forestry Research, v. 34, n. 1, p. 269-282, 2023. 1993-0607 1007-662X http://hdl.handle.net/11449/247855 10.1007/s11676-022-01557-3 2-s2.0-85141680342 |
url |
http://dx.doi.org/10.1007/s11676-022-01557-3 http://hdl.handle.net/11449/247855 |
identifier_str_mv |
Journal of Forestry Research, v. 34, n. 1, p. 269-282, 2023. 1993-0607 1007-662X 10.1007/s11676-022-01557-3 2-s2.0-85141680342 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of Forestry Research |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
269-282 |
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_ |
1808129297740726272 |