Stem profile description in plantations for different species using artificial neural network
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , |
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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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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1783370935956406272 |