Stem profile description in plantations for different species using artificial neural network

Detalhes bibliográficos
Autor(a) principal: Campos, Bráulio Pizziôlo Furtado
Data de Publicação: 2017
Outros Autores: Silva, Gilson Fernandes da, Binoti, Daniel Henrique Breda, Mendonça, Adriano Ribeiro de, Leite, Helio Garcia
Tipo de documento: Artigo
Idioma: por
Título da fonte: Pesquisa Florestal Brasileira (Online)
Texto Completo: https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1181
Resumo: The objective of this study was to analyze the ability of an artificial neural network (ANN) to describe the stem profile of trees of different genera and species in different growing conditions. For comparative purposes, equations were fit, using regression analysis to describe the stem profile. For neural network as well as for the regression equations, evaluation of accuracy was based on correlation coefficient between observed and estimated diameters along the stem, square root of the mean square percentage error (RMSE) and graphical analysis. Artificial intelligence methods, especially ANN, can be effective in describing trees bole profile of different species in different growth conditions using only one ANN with similar efficiency as regression models traditionally employed by forestry companies.
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spelling Stem profile description in plantations for different species using artificial neural networkDescrição do perfil do tronco de árvores em plantios de diferentes espécies por meio de redes neurais artificiaisInventário FlorestalManejo FlorestalInteligência artificialForest inventoryForest managementArtificial intelligenceThe objective of this study was to analyze the ability of an artificial neural network (ANN) to describe the stem profile of trees of different genera and species in different growing conditions. For comparative purposes, equations were fit, using regression analysis to describe the stem profile. For neural network as well as for the regression equations, evaluation of accuracy was based on correlation coefficient between observed and estimated diameters along the stem, square root of the mean square percentage error (RMSE) and graphical analysis. Artificial intelligence methods, especially ANN, can be effective in describing trees bole profile of different species in different growth conditions using only one ANN with similar efficiency as regression models traditionally employed by forestry companies.O objetivo deste trabalho foi analisar a capacidade de uma rede neural artificial (RNA) em descrever o perfil do fuste de árvores de diferentes gêneros e espécies em diferentes condições de crescimento. Para fins comparativos, foram ajustadas equações, empregando-se análise de regressão, para descrever o perfil do tronco. Tanto para as redes neurais quanto para as equações de regressão, a avaliação da acurácia foi realizada com base no coeficiente de correlação entre os diâmetros observados e estimados ao longo do fuste, a raiz quadrada do erro quadrático médio percentual (RMSE) e análise gráfica. Os métodos de inteligência artificial, especialmente RNA, podem ser eficazes em descrever o perfil do fuste de árvores de diferentes espécies em diferentes condições de crescimento, utilizando apenas uma RNA, com eficiência semelhante aos modelos de regressão tradicionalmente empregados por empresas florestais.Embrapa Florestas2017-06-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/118110.4336/2017.pfb.37.90.1181Pesquisa Florestal Brasileira; v. 37 n. 90 (2017): abr./jun.; 99-107Pesquisa Florestal Brasileira; Vol. 37 No. 90 (2017): abr./jun.; 99-1071983-26051809-3647reponame:Pesquisa Florestal Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAporhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1181/564Copyright (c) 2017 Bráulio Pizziôlo Furtado Campos, Gilson Fernandes da Silva, Daniel Henrique Breda Binoti, Adriano Ribeiro de Mendonça, Helio Garcia Leitehttps://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessCampos, Bráulio Pizziôlo FurtadoSilva, Gilson Fernandes daBinoti, Daniel Henrique BredaMendonça, Adriano Ribeiro deLeite, Helio Garcia2018-01-07T19:01:10Zoai:pfb.cnpf.embrapa.br/pfb:article/1181Revistahttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/PUBhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/oaipfb@embrapa.br || revista.pfb@gmail.com || patricia.mattos@embrapa.br1983-26051809-3647opendoar:2018-01-07T19:01:10Pesquisa Florestal Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Stem profile description in plantations for different species using artificial neural network
Descrição do perfil do tronco de árvores em plantios de diferentes espécies por meio de redes neurais artificiais
title Stem profile description in plantations for different species using artificial neural network
spellingShingle Stem profile description in plantations for different species using artificial neural network
Campos, Bráulio Pizziôlo Furtado
Inventário Florestal
Manejo Florestal
Inteligência artificial
Forest inventory
Forest management
Artificial intelligence
title_short Stem profile description in plantations for different species using artificial neural network
title_full Stem profile description in plantations for different species using artificial neural network
title_fullStr Stem profile description in plantations for different species using artificial neural network
title_full_unstemmed Stem profile description in plantations for different species using artificial neural network
title_sort Stem profile description in plantations for different species using artificial neural network
author Campos, Bráulio Pizziôlo Furtado
author_facet Campos, Bráulio Pizziôlo Furtado
Silva, Gilson Fernandes da
Binoti, Daniel Henrique Breda
Mendonça, Adriano Ribeiro de
Leite, Helio Garcia
author_role author
author2 Silva, Gilson Fernandes da
Binoti, Daniel Henrique Breda
Mendonça, Adriano Ribeiro de
Leite, Helio Garcia
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Campos, Bráulio Pizziôlo Furtado
Silva, Gilson Fernandes da
Binoti, Daniel Henrique Breda
Mendonça, Adriano Ribeiro de
Leite, Helio Garcia
dc.subject.por.fl_str_mv Inventário Florestal
Manejo Florestal
Inteligência artificial
Forest inventory
Forest management
Artificial intelligence
topic Inventário Florestal
Manejo Florestal
Inteligência artificial
Forest inventory
Forest management
Artificial intelligence
description The objective of this study was to analyze the ability of an artificial neural network (ANN) to describe the stem profile of trees of different genera and species in different growing conditions. For comparative purposes, equations were fit, using regression analysis to describe the stem profile. For neural network as well as for the regression equations, evaluation of accuracy was based on correlation coefficient between observed and estimated diameters along the stem, square root of the mean square percentage error (RMSE) and graphical analysis. Artificial intelligence methods, especially ANN, can be effective in describing trees bole profile of different species in different growth conditions using only one ANN with similar efficiency as regression models traditionally employed by forestry companies.
publishDate 2017
dc.date.none.fl_str_mv 2017-06-30
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 https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1181
10.4336/2017.pfb.37.90.1181
url https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1181
identifier_str_mv 10.4336/2017.pfb.37.90.1181
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1181/564
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Embrapa Florestas
publisher.none.fl_str_mv Embrapa Florestas
dc.source.none.fl_str_mv Pesquisa Florestal Brasileira; v. 37 n. 90 (2017): abr./jun.; 99-107
Pesquisa Florestal Brasileira; Vol. 37 No. 90 (2017): abr./jun.; 99-107
1983-2605
1809-3647
reponame:Pesquisa Florestal Brasileira (Online)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Pesquisa Florestal Brasileira (Online)
collection Pesquisa Florestal Brasileira (Online)
repository.name.fl_str_mv Pesquisa Florestal Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv pfb@embrapa.br || revista.pfb@gmail.com || patricia.mattos@embrapa.br
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