Random forest model to predict the height of Eucalyptus.

Detalhes bibliográficos
Autor(a) principal: LIMA, E. de S.
Data de Publicação: 2022
Outros Autores: SOUZA, Z. M. de, OLIVEIRA, S. R. de M., MONTANARI, R., FARHATE, C. V. 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/1141899
http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210153/2022
Resumo: Eucalyptus (Eucalyptus urograndis) production has significantly advanced over the past few years in Brazil, especially with regard to acreage and productivity. Machine learning has made significant advances in most varied fields of agrarian sciences. In this context, this study aimed to use physicochemical variables of the soil as well as climatic and dendrometric variables of eucalyptus to predict its height using the random forest algorithm. The study was conducted in the municipality of Três Lagoas, in Mato Grosso do Sul, Brazil.
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spelling Random forest model to predict the height of Eucalyptus.Variáveis físico-químicas do soloAprendizado de máquinaConteúdo de fósforo no soloMistura de solosAlumínio permutávelEucalyptus urograndisFloresta aleatóriaCrescimento de eucaliptoPhysicochemical variables of soilMachine learningSoil phosphorus contentSoil moistureEucalyptusExchangeable aluminumEucalyptus (Eucalyptus urograndis) production has significantly advanced over the past few years in Brazil, especially with regard to acreage and productivity. Machine learning has made significant advances in most varied fields of agrarian sciences. In this context, this study aimed to use physicochemical variables of the soil as well as climatic and dendrometric variables of eucalyptus to predict its height using the random forest algorithm. The study was conducted in the municipality of Três Lagoas, in Mato Grosso do Sul, Brazil.Special issue: artificial intelligence.ELIZEU DE S. LIMA, FEAGRI/UNICAMP; ZIGOMAR MENEZES DE SOUZA, FEAGRI/UNICAMP; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; RAFAEL MONTANARI, UNESP; CAMILA VIANA VIEIRA FARHATE, FCAV/UNESP.LIMA, E. de S.SOUZA, Z. M. deOLIVEIRA, S. R. de M.MONTANARI, R.FARHATE, C. V. V.2022-04-06T12:05:44Z2022-04-06T12:05:44Z2022-04-062022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleEngenharia Agrícola, v. 42, e20210153, 2022.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1141899http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210153/2022enginfo: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:EMBRAPA2022-04-06T12:05:53Zoai:www.alice.cnptia.embrapa.br:doc/1141899Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542022-04-06T12:05:53falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542022-04-06T12:05:53Repositório 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 Random forest model to predict the height of Eucalyptus.
title Random forest model to predict the height of Eucalyptus.
spellingShingle Random forest model to predict the height of Eucalyptus.
LIMA, E. de S.
Variáveis físico-químicas do solo
Aprendizado de máquina
Conteúdo de fósforo no solo
Mistura de solos
Alumínio permutável
Eucalyptus urograndis
Floresta aleatória
Crescimento de eucalipto
Physicochemical variables of soil
Machine learning
Soil phosphorus content
Soil moisture
Eucalyptus
Exchangeable aluminum
title_short Random forest model to predict the height of Eucalyptus.
title_full Random forest model to predict the height of Eucalyptus.
title_fullStr Random forest model to predict the height of Eucalyptus.
title_full_unstemmed Random forest model to predict the height of Eucalyptus.
title_sort Random forest model to predict the height of Eucalyptus.
author LIMA, E. de S.
author_facet LIMA, E. de S.
SOUZA, Z. M. de
OLIVEIRA, S. R. de M.
MONTANARI, R.
FARHATE, C. V. V.
author_role author
author2 SOUZA, Z. M. de
OLIVEIRA, S. R. de M.
MONTANARI, R.
FARHATE, C. V. V.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv ELIZEU DE S. LIMA, FEAGRI/UNICAMP; ZIGOMAR MENEZES DE SOUZA, FEAGRI/UNICAMP; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; RAFAEL MONTANARI, UNESP; CAMILA VIANA VIEIRA FARHATE, FCAV/UNESP.
dc.contributor.author.fl_str_mv LIMA, E. de S.
SOUZA, Z. M. de
OLIVEIRA, S. R. de M.
MONTANARI, R.
FARHATE, C. V. V.
dc.subject.por.fl_str_mv Variáveis físico-químicas do solo
Aprendizado de máquina
Conteúdo de fósforo no solo
Mistura de solos
Alumínio permutável
Eucalyptus urograndis
Floresta aleatória
Crescimento de eucalipto
Physicochemical variables of soil
Machine learning
Soil phosphorus content
Soil moisture
Eucalyptus
Exchangeable aluminum
topic Variáveis físico-químicas do solo
Aprendizado de máquina
Conteúdo de fósforo no solo
Mistura de solos
Alumínio permutável
Eucalyptus urograndis
Floresta aleatória
Crescimento de eucalipto
Physicochemical variables of soil
Machine learning
Soil phosphorus content
Soil moisture
Eucalyptus
Exchangeable aluminum
description Eucalyptus (Eucalyptus urograndis) production has significantly advanced over the past few years in Brazil, especially with regard to acreage and productivity. Machine learning has made significant advances in most varied fields of agrarian sciences. In this context, this study aimed to use physicochemical variables of the soil as well as climatic and dendrometric variables of eucalyptus to predict its height using the random forest algorithm. The study was conducted in the municipality of Três Lagoas, in Mato Grosso do Sul, Brazil.
publishDate 2022
dc.date.none.fl_str_mv 2022-04-06T12:05:44Z
2022-04-06T12:05:44Z
2022-04-06
2022
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, v. 42, e20210153, 2022.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1141899
http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210153/2022
identifier_str_mv Engenharia Agrícola, v. 42, e20210153, 2022.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1141899
http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210153/2022
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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