White mold detection in common beans through leaf reflectance spectroscopy
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , |
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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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 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162015000601117 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162015000601117 |
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 |
eu_rights_str_mv |
openAccess |
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) |
instacron_str |
SBEA |
institution |
SBEA |
reponame_str |
Engenharia Agrícola |
collection |
Engenharia Agrícola |
repository.name.fl_str_mv |
Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA) |
repository.mail.fl_str_mv |
revistasbea@sbea.org.br||sbea@sbea.org.br |
_version_ |
1752126272444563456 |