Random forest model to predict the height of Eucalyptus.

LIMA, E. de S.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; MONTANARI, R.; FARHATE, C. V. V.; 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.

Random forest model to predict the height of Eucalyptus.

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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Bibliographic Details
Main Authors:	LIMA, E. de S., SOUZA, Z. M. de, OLIVEIRA, S. R. de M., MONTANARI, R., FARHATE, C. V. V.
Other Authors:	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.
Format:	Artigo de periódico biblioteca
Language:	Ingles English
Published:	2022-04-06
Subjects:	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,
Online Access:	http://www.alice.cnptia.embrapa.br/alice/handle/doc/1141899 http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210153/2022
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id	dig-alice-doc-1141899
record_format	koha
spelling	dig-alice-doc-11418992022-04-06T12:05:53Z Random forest model to predict the height of Eucalyptus. LIMA, E. de S. SOUZA, Z. M. de OLIVEIRA, S. R. de M. MONTANARI, R. FARHATE, C. V. V. 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. 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 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. Special issue: artificial intelligence. 2022-04-06T12:05:44Z 2022-04-06T12:05:44Z 2022-04-06 2022 Artigo de periódico 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 Ingles en openAccess
institution	EMBRAPA
collection	DSpace
country	Brasil
countrycode	BR
component	Bibliográfico
access	En linea
databasecode	dig-alice
tag	biblioteca
region	America del Sur
libraryname	Sistema de bibliotecas de EMBRAPA
language	Ingles English
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 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
spellingShingle	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 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 LIMA, E. de S. SOUZA, Z. M. de OLIVEIRA, S. R. de M. MONTANARI, R. FARHATE, C. V. V. Random forest model to predict the height of Eucalyptus.
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.
author2	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.
author_facet	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. de OLIVEIRA, S. R. de M. MONTANARI, R. FARHATE, C. V. V.
format	Artigo de periódico
topic_facet	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
author	LIMA, E. de S. SOUZA, Z. M. de OLIVEIRA, S. R. de M. MONTANARI, R. FARHATE, C. V. V.
author_sort	LIMA, E. de S.
title	Random forest model to predict the height of Eucalyptus.
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.
publishDate	2022-04-06
url	http://www.alice.cnptia.embrapa.br/alice/handle/doc/1141899 http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210153/2022
work_keys_str_mv	AT limaedes randomforestmodeltopredicttheheightofeucalyptus AT souzazmde randomforestmodeltopredicttheheightofeucalyptus AT oliveirasrdem randomforestmodeltopredicttheheightofeucalyptus AT montanarir randomforestmodeltopredicttheheightofeucalyptus AT farhatecvv randomforestmodeltopredicttheheightofeucalyptus
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Random forest model to predict the height of Eucalyptus.

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