Random forest model to predict the height of Eucalyptus.
Random forest model to predict the height of Eucalyptus.
Author(s): LIMA, E. de S.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; MONTANARI, R.; FARHATE, C. V. V.
Summary: 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.
Publication year: 2022
Types of publication: Journal article
Keywords: Alumínio permutável, Aprendizado de máquina, Conteúdo de fósforo no solo, Crescimento de eucalipto, Eucalyptus, Eucalyptus urograndis, Exchangeable aluminum, Floresta aleatória, Machine learning, Mistura de solos, Physicochemical variables of soil, Soil moisture, Soil phosphorus content, Variáveis físico-químicas do solo
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