Random forest model to predict the height of Eucalyptus.
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
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/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. |
id |
EMBR_0a7761c515ff143dc8f41d83dfbeed78 |
---|---|
oai_identifier_str |
oai:www.alice.cnptia.embrapa.br:doc/1141899 |
network_acronym_str |
EMBR |
network_name_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository_id_str |
2154 |
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 |
_version_ |
1794503520765870080 |