Title:	Random forest model to predict the height of Eucalyptus.
Authors:	LIMA, E. de S. SOUZA, Z. M. de OLIVEIRA, S. R. de M. MONTANARI, R. FARHATE, C. V. V.
Affiliation:	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.
Date Issued:	2022
Citation:	Engenharia Agrícola, v. 42, e20210153, 2022.
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.
NAL Thesaurus:	Eucalyptus Exchangeable aluminum
Keywords:	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
DOI:	http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210153/2022
Notes:	Special issue: artificial intelligence.
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPTIA)