White mold detection in common beans through leaf reflectance spectroscopy

Detalhes bibliográficos
Autor(a) principal: Machado,Marley L.
Data de Publicação: 2015
Outros Autores: Pinto,Francisco de A. C., Paula Junior,Trazilbo J. de, Queiroz,Daniel M. de, Cerqueira,Ozires de A. T.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Engenharia Agrícola
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162015000601117
Resumo: ABSTRACT This study aimed to identify wavelengths based on leaf reflectance (400-1050 nm) to estimate white mold severity in common beans at different seasons. Two experiments were carried out, one during fall and another in winter. Partial Least Squares (PLS) regression was used to establish a set of wavelengths that better estimates the disease severity at a specific date. Therefore, observations were previously divided in two sub-groups. The first one (calibration) was used for model building and the second subgroup for model testing. Error measurements and correlation between measured and predicted values of disease severity index were employed to provide the best wavelengths in both seasons. The average indexes of each experiment were of 5.8% and 7.4%, which is considered low. Spectral bands ranged between blue and green, green and red, and red and infrared, being most sensitive for disease estimation. Beyond the transition ranges, other spectral regions also presented wavelengths with potential to determine the disease severity, such as red, green, and near infrared.
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spelling White mold detection in common beans through leaf reflectance spectroscopySclerotinia sclerotiorumPLS regressionpigmentsspectrumabsorptionABSTRACT This study aimed to identify wavelengths based on leaf reflectance (400-1050 nm) to estimate white mold severity in common beans at different seasons. Two experiments were carried out, one during fall and another in winter. Partial Least Squares (PLS) regression was used to establish a set of wavelengths that better estimates the disease severity at a specific date. Therefore, observations were previously divided in two sub-groups. The first one (calibration) was used for model building and the second subgroup for model testing. Error measurements and correlation between measured and predicted values of disease severity index were employed to provide the best wavelengths in both seasons. The average indexes of each experiment were of 5.8% and 7.4%, which is considered low. Spectral bands ranged between blue and green, green and red, and red and infrared, being most sensitive for disease estimation. Beyond the transition ranges, other spectral regions also presented wavelengths with potential to determine the disease severity, such as red, green, and near infrared.Associação Brasileira de Engenharia Agrícola2015-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162015000601117Engenharia Agrícola v.35 n.6 2015reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/1809-4430-Eng.Agric.v35n6p1117-1126/2015info:eu-repo/semantics/openAccessMachado,Marley L.Pinto,Francisco de A. C.Paula Junior,Trazilbo J. deQueiroz,Daniel M. deCerqueira,Ozires de A. T.eng2016-03-01T00:00:00Zoai:scielo:S0100-69162015000601117Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2016-03-01T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false
dc.title.none.fl_str_mv White mold detection in common beans through leaf reflectance spectroscopy
title White mold detection in common beans through leaf reflectance spectroscopy
spellingShingle White mold detection in common beans through leaf reflectance spectroscopy
Machado,Marley L.
Sclerotinia sclerotiorum
PLS regression
pigments
spectrum
absorption
title_short White mold detection in common beans through leaf reflectance spectroscopy
title_full White mold detection in common beans through leaf reflectance spectroscopy
title_fullStr White mold detection in common beans through leaf reflectance spectroscopy
title_full_unstemmed White mold detection in common beans through leaf reflectance spectroscopy
title_sort White mold detection in common beans through leaf reflectance spectroscopy
author Machado,Marley L.
author_facet Machado,Marley L.
Pinto,Francisco de A. C.
Paula Junior,Trazilbo J. de
Queiroz,Daniel M. de
Cerqueira,Ozires de A. T.
author_role author
author2 Pinto,Francisco de A. C.
Paula Junior,Trazilbo J. de
Queiroz,Daniel M. de
Cerqueira,Ozires de A. T.
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Machado,Marley L.
Pinto,Francisco de A. C.
Paula Junior,Trazilbo J. de
Queiroz,Daniel M. de
Cerqueira,Ozires de A. T.
dc.subject.por.fl_str_mv Sclerotinia sclerotiorum
PLS regression
pigments
spectrum
absorption
topic Sclerotinia sclerotiorum
PLS regression
pigments
spectrum
absorption
description ABSTRACT This study aimed to identify wavelengths based on leaf reflectance (400-1050 nm) to estimate white mold severity in common beans at different seasons. Two experiments were carried out, one during fall and another in winter. Partial Least Squares (PLS) regression was used to establish a set of wavelengths that better estimates the disease severity at a specific date. Therefore, observations were previously divided in two sub-groups. The first one (calibration) was used for model building and the second subgroup for model testing. Error measurements and correlation between measured and predicted values of disease severity index were employed to provide the best wavelengths in both seasons. The average indexes of each experiment were of 5.8% and 7.4%, which is considered low. Spectral bands ranged between blue and green, green and red, and red and infrared, being most sensitive for disease estimation. Beyond the transition ranges, other spectral regions also presented wavelengths with potential to determine the disease severity, such as red, green, and near infrared.
publishDate 2015
dc.date.none.fl_str_mv 2015-12-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162015000601117
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1809-4430-Eng.Agric.v35n6p1117-1126/2015
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Engenharia Agrícola
publisher.none.fl_str_mv Associação Brasileira de Engenharia Agrícola
dc.source.none.fl_str_mv Engenharia Agrícola v.35 n.6 2015
reponame:Engenharia Agrícola
instname:Associação Brasileira de Engenharia Agrícola (SBEA)
instacron:SBEA
instname_str Associação Brasileira de Engenharia Agrícola (SBEA)
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reponame_str Engenharia Agrícola
collection Engenharia Agrícola
repository.name.fl_str_mv Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)
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