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

Detalhes bibliográficos
Autor(a) principal: LAMPARELLI, R. A. C.
Data de Publicação: 2012
Outros Autores: JOHANN, J. A., SANTOS, É. R. dos, ESQUERDO, J. C. D. M., ROCHA, J. V.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/924115
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 plants.Data miningMineração de dadosMonitoramento de culturaComportamento espectralManejoSensoriamento RemotoCrop managementRemote 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.RUBENS A. C. LAMPARELLI, Cepagri/Unicamp; JERRY A. JOHANN, Feagri/Unicamp; ÉDER R. DOS SANTOS, Cooxupé; JULIO C. D. M. ESQUERDO, CNPTIA; JANSLE V. ROCHA, Feagri/Unicamp.LAMPARELLI, R. A. C.JOHANN, J. A.SANTOS, É. R. dosESQUERDO, J. C. D. M.ROCHA, J. V.2012-05-08T11:11:11Z2012-05-08T11:11:11Z2012-05-08T11:11:11Z2012-05-08T11:11:11Z2012-05-0820122012-05-08T11:11:11Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleEngenharia Agrícola, Jaboticabal, v. 32, n. 1, p. 184-196, jan./fev. 2012.http://www.alice.cnptia.embrapa.br/alice/handle/doc/924115enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2017-08-16T00:30:44Zoai:www.alice.cnptia.embrapa.br:doc/924115Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542017-08-16T00:30:44falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542017-08-16T00:30:44Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)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, R. A. C.
Data mining
Mineração de dados
Monitoramento de cultura
Comportamento espectral
Manejo
Sensoriamento Remoto
Crop management
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, R. A. C.
author_facet LAMPARELLI, R. A. C.
JOHANN, J. A.
SANTOS, É. R. dos
ESQUERDO, J. C. D. M.
ROCHA, J. V.
author_role author
author2 JOHANN, J. A.
SANTOS, É. R. dos
ESQUERDO, J. C. D. M.
ROCHA, J. V.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv RUBENS A. C. LAMPARELLI, Cepagri/Unicamp; JERRY A. JOHANN, Feagri/Unicamp; ÉDER R. DOS SANTOS, Cooxupé; JULIO C. D. M. ESQUERDO, CNPTIA; JANSLE V. ROCHA, Feagri/Unicamp.
dc.contributor.author.fl_str_mv LAMPARELLI, R. A. C.
JOHANN, J. A.
SANTOS, É. R. dos
ESQUERDO, J. C. D. M.
ROCHA, J. V.
dc.subject.por.fl_str_mv Data mining
Mineração de dados
Monitoramento de cultura
Comportamento espectral
Manejo
Sensoriamento Remoto
Crop management
Remote sensing
topic Data mining
Mineração de dados
Monitoramento de cultura
Comportamento espectral
Manejo
Sensoriamento Remoto
Crop management
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 2012-05-08T11:11:11Z
2012-05-08T11:11:11Z
2012-05-08T11:11:11Z
2012-05-08T11:11:11Z
2012-05-08
2012
2012-05-08T11:11:11Z
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Engenharia Agrícola, Jaboticabal, v. 32, n. 1, p. 184-196, jan./fev. 2012.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/924115
identifier_str_mv Engenharia Agrícola, Jaboticabal, v. 32, n. 1, p. 184-196, jan./fev. 2012.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/924115
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
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