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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| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Artigo de periódico biblioteca |
| Language: | Ingles English |
| Published: |
2022-04-06
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| 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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