Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

Detalhes bibliográficos
Autor(a) principal: Lamparelli,Rubens A. C.
Data de Publicação: 2012
Outros Autores: Johann,Jerry A., Santos,Éder R. dos, Esquerdo,Julio C. D. M., Rocha,Jansle V.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Engenharia Agrícola
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162012000100019
Resumo: This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.
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spelling Use of data mining and spectral profiles to differentiate condition after harvest of coffee plantscrop monitoringspectral behaviormanagementorbital remote sensingThis study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.Associação Brasileira de Engenharia Agrícola2012-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162012000100019Engenharia Agrícola v.32 n.1 2012reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/S0100-69162012000100019info:eu-repo/semantics/openAccessLamparelli,Rubens A. C.Johann,Jerry A.Santos,Éder R. dosEsquerdo,Julio C. D. M.Rocha,Jansle V.eng2012-04-17T00:00:00Zoai:scielo:S0100-69162012000100019Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2012-04-17T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false
dc.title.none.fl_str_mv Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants
title Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants
spellingShingle Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants
Lamparelli,Rubens A. C.
crop monitoring
spectral behavior
management
orbital remote sensing
title_short Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants
title_full Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants
title_fullStr Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants
title_full_unstemmed Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants
title_sort Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants
author Lamparelli,Rubens A. C.
author_facet Lamparelli,Rubens A. C.
Johann,Jerry A.
Santos,Éder R. dos
Esquerdo,Julio C. D. M.
Rocha,Jansle V.
author_role author
author2 Johann,Jerry A.
Santos,Éder R. dos
Esquerdo,Julio C. D. M.
Rocha,Jansle V.
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Lamparelli,Rubens A. C.
Johann,Jerry A.
Santos,Éder R. dos
Esquerdo,Julio C. D. M.
Rocha,Jansle V.
dc.subject.por.fl_str_mv crop monitoring
spectral behavior
management
orbital remote sensing
topic crop monitoring
spectral behavior
management
orbital remote sensing
description This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.
publishDate 2012
dc.date.none.fl_str_mv 2012-02-01
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=S0100-69162012000100019
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162012000100019
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0100-69162012000100019
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.32 n.1 2012
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
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