Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants.
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , , |
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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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 |
_version_ |
1794503362959376384 |