Application of artificial neural network in the classification of coffee áreas in Machado, Minas Gerais State

Detalhes bibliográficos
Autor(a) principal: Andrade, Livia Naiara
Data de Publicação: 2013
Outros Autores: Vieira, Tatiana Grossi Chquiloff, Lacerda, Wilian Soares, Volpato, Margarete Marin Lordelo, Davis Júnior, Clodoveu Augusto
Tipo de documento: Artigo
Idioma: eng
por
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/13614
http://www.coffeescience.ufla.br/index.php/Coffeescience/article/view/363
Resumo: The coffee is extremely important activity in southern of Minas Gerais and techniques for estimating acreage, seeking reliable crop forecasts are being intensely investigated. It is presented in this study, an application of Artificial Neural Networks (ANN) for the automatic classification of remote sensing data in order to identify areas of the coffee region Machado, Minas Gerais. The methodology for developing the application of RNA was divided intothree stages: pre-processing of data, training and use of RNA, and analysis of results. The first step was performed dividing the study area into two parts (one embossed busiest and least busy one with relief), because this region has a strong emphasis smooth wavy, causing a greater difficulty of automatic mapping of use earth from satelliteimages. Masks were also created in the drainage network and the urban area. In the second step, various RNA’s were trained from several samples representative of the classes of images of interest and was made to classify the rest ofthe image obtained using the best RNA. The third step consisted in analyzing and validating the results, performing across between the classified map and the map visually classified by neural network chosen. We used the Kappa index to evaluate the performance of the RNA, since the use of this coefficient is satisfactory to assess the accuracy of a thematic classification. The result was higher than the results reported in the literature, with a Kappa index of 0.558 to 0.602 relief busiest and least busy for relief.
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spelling Application of artificial neural network in the classification of coffee áreas in Machado, Minas Gerais StateArtificial neural networksCafeiculturaRedes neurais artificiaisAutomatic classificationCoffeeGeoprocessamentoCafeiculturaThe coffee is extremely important activity in southern of Minas Gerais and techniques for estimating acreage, seeking reliable crop forecasts are being intensely investigated. It is presented in this study, an application of Artificial Neural Networks (ANN) for the automatic classification of remote sensing data in order to identify areas of the coffee region Machado, Minas Gerais. The methodology for developing the application of RNA was divided intothree stages: pre-processing of data, training and use of RNA, and analysis of results. The first step was performed dividing the study area into two parts (one embossed busiest and least busy one with relief), because this region has a strong emphasis smooth wavy, causing a greater difficulty of automatic mapping of use earth from satelliteimages. Masks were also created in the drainage network and the urban area. In the second step, various RNA’s were trained from several samples representative of the classes of images of interest and was made to classify the rest ofthe image obtained using the best RNA. The third step consisted in analyzing and validating the results, performing across between the classified map and the map visually classified by neural network chosen. We used the Kappa index to evaluate the performance of the RNA, since the use of this coefficient is satisfactory to assess the accuracy of a thematic classification. The result was higher than the results reported in the literature, with a Kappa index of 0.558 to 0.602 relief busiest and least busy for relief.A cafeicultura é atividade de fundamental importância na região sul de Minas Gerais e técnicas de estimativa da área plantada, visando previsões de safra confiáveis, estão sendo intensamente pesquisadas. Apresenta-se,no presente estudo, uma aplicação de Redes Neurais Artificiais (RNA) para a classificação automática de dados de sensoriamento remoto, objetivando identificar áreas cafeeiras da região de Machado, MG. A metodologia para desenvolvimento da aplicação da RNA foi dividida em três etapas: pré-processamento dos dados; treinamento e uso da RNA; e análise dos resultados. Na primeira etapa foi realizada a divisão da área em estudo em duas partes (uma com relevo mais movimentado e outra com relevo menos movimentado), isso porque a região apresenta relevo suave ondulado a forte ondulado, o que acarreta maior dificuldade do mapeamento automático do uso da terra a partir de imagens de satélite. Foram também criadas máscaras na rede de drenagem e área urbana. Na segunda etapa, diversas RNAs foram treinadas a partir de várias amostras de imagens representativas das classes de interesse e foi feita a classificação do restante da imagem utilizando a melhor RNA obtida. A terceira etapa consistiu na análise e validação dos resultados, realizando um cruzamento entre o mapa classificado visualmente e o mapa classificado pela Rede Neural escolhida. Utilizou-se o índice Kappa para avaliar o desempenho da RNA, uma vez que o uso desse coeficiente é satisfatório na avaliação da precisão de uma classificação temática. O resultado obtido foi superior aos resultados encontrados na literatura, com um índice Kappa de 0,558 para o relevo mais movimentado e 0,602 para o relevo menos movimentado.2013-04-212017-08-01T20:06:12Z2017-08-01T20:06:12Z2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfapplication/pdfANDRADE, L. N. et al. Application of artificial neural network in the classification of coffee areas in Machado, Minas Gerais State. Coffee Science, Lavras, v. 8, n. 1, p. 71-81, jan./mar. 2013.http://repositorio.ufla.br/jspui/handle/1/13614http://www.coffeescience.ufla.br/index.php/Coffeescience/article/view/363Coffee Science; v. 8, n. 1 (2013); 78-90Coffee Science; v. 8, n. 1 (2013); 78-90Coffee Science; v. 8, n. 1 (2013); 78-901984-39091809-6875reponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAengporhttp://www.coffeescience.ufla.br/index.php/Coffeescience/article/view/363/pdf_119http://www.coffeescience.ufla.br/index.php/Coffeescience/article/view/363/pdfAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessAndrade, Livia NaiaraVieira, Tatiana Grossi ChquiloffLacerda, Wilian SoaresVolpato, Margarete Marin LordeloDavis Júnior, Clodoveu AugustoAndrade, Livia NaiaraVieira, Tatiana Grossi ChquiloffLacerda, Wilian SoaresVolpato, Margarete Marin LordeloDavis Júnior, Clodoveu Augusto2021-01-15T17:58:45Zoai:localhost:1/13614Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2021-01-15T17:58:45Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Application of artificial neural network in the classification of coffee áreas in Machado, Minas Gerais State
title Application of artificial neural network in the classification of coffee áreas in Machado, Minas Gerais State
spellingShingle Application of artificial neural network in the classification of coffee áreas in Machado, Minas Gerais State
Andrade, Livia Naiara
Artificial neural networks
Cafeicultura
Redes neurais artificiais
Automatic classification
Coffee
Geoprocessamento
Cafeicultura
title_short Application of artificial neural network in the classification of coffee áreas in Machado, Minas Gerais State
title_full Application of artificial neural network in the classification of coffee áreas in Machado, Minas Gerais State
title_fullStr Application of artificial neural network in the classification of coffee áreas in Machado, Minas Gerais State
title_full_unstemmed Application of artificial neural network in the classification of coffee áreas in Machado, Minas Gerais State
title_sort Application of artificial neural network in the classification of coffee áreas in Machado, Minas Gerais State
author Andrade, Livia Naiara
author_facet Andrade, Livia Naiara
Vieira, Tatiana Grossi Chquiloff
Lacerda, Wilian Soares
Volpato, Margarete Marin Lordelo
Davis Júnior, Clodoveu Augusto
author_role author
author2 Vieira, Tatiana Grossi Chquiloff
Lacerda, Wilian Soares
Volpato, Margarete Marin Lordelo
Davis Júnior, Clodoveu Augusto
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Andrade, Livia Naiara
Vieira, Tatiana Grossi Chquiloff
Lacerda, Wilian Soares
Volpato, Margarete Marin Lordelo
Davis Júnior, Clodoveu Augusto
Andrade, Livia Naiara
Vieira, Tatiana Grossi Chquiloff
Lacerda, Wilian Soares
Volpato, Margarete Marin Lordelo
Davis Júnior, Clodoveu Augusto
dc.subject.por.fl_str_mv Artificial neural networks
Cafeicultura
Redes neurais artificiais
Automatic classification
Coffee
Geoprocessamento
Cafeicultura
topic Artificial neural networks
Cafeicultura
Redes neurais artificiais
Automatic classification
Coffee
Geoprocessamento
Cafeicultura
description The coffee is extremely important activity in southern of Minas Gerais and techniques for estimating acreage, seeking reliable crop forecasts are being intensely investigated. It is presented in this study, an application of Artificial Neural Networks (ANN) for the automatic classification of remote sensing data in order to identify areas of the coffee region Machado, Minas Gerais. The methodology for developing the application of RNA was divided intothree stages: pre-processing of data, training and use of RNA, and analysis of results. The first step was performed dividing the study area into two parts (one embossed busiest and least busy one with relief), because this region has a strong emphasis smooth wavy, causing a greater difficulty of automatic mapping of use earth from satelliteimages. Masks were also created in the drainage network and the urban area. In the second step, various RNA’s were trained from several samples representative of the classes of images of interest and was made to classify the rest ofthe image obtained using the best RNA. The third step consisted in analyzing and validating the results, performing across between the classified map and the map visually classified by neural network chosen. We used the Kappa index to evaluate the performance of the RNA, since the use of this coefficient is satisfactory to assess the accuracy of a thematic classification. The result was higher than the results reported in the literature, with a Kappa index of 0.558 to 0.602 relief busiest and least busy for relief.
publishDate 2013
dc.date.none.fl_str_mv 2013-04-21
2013
2017-08-01T20:06:12Z
2017-08-01T20:06:12Z
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv ANDRADE, L. N. et al. Application of artificial neural network in the classification of coffee areas in Machado, Minas Gerais State. Coffee Science, Lavras, v. 8, n. 1, p. 71-81, jan./mar. 2013.
http://repositorio.ufla.br/jspui/handle/1/13614
http://www.coffeescience.ufla.br/index.php/Coffeescience/article/view/363
identifier_str_mv ANDRADE, L. N. et al. Application of artificial neural network in the classification of coffee areas in Machado, Minas Gerais State. Coffee Science, Lavras, v. 8, n. 1, p. 71-81, jan./mar. 2013.
url http://repositorio.ufla.br/jspui/handle/1/13614
http://www.coffeescience.ufla.br/index.php/Coffeescience/article/view/363
dc.language.iso.fl_str_mv eng
por
language eng
por
dc.relation.none.fl_str_mv http://www.coffeescience.ufla.br/index.php/Coffeescience/article/view/363/pdf_119
http://www.coffeescience.ufla.br/index.php/Coffeescience/article/view/363/pdf
dc.rights.driver.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.source.none.fl_str_mv Coffee Science; v. 8, n. 1 (2013); 78-90
Coffee Science; v. 8, n. 1 (2013); 78-90
Coffee Science; v. 8, n. 1 (2013); 78-90
1984-3909
1809-6875
reponame:Repositório Institucional da UFLA
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institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
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