01709naa a2200361 a 450000100080000000500110000800800410001902400760006010000190013624500840015526000090023950000440024852005440029265000150083665000260085165300260087765300280090365300340093165300290096565300260099465300240102065300210104465300210106565300380108665300180112465300280114265300410117070000200121170000260123170000180125770000220127577300500129721418992022-04-06       2022    bl uuuu     u00u1 u    #d7 ahttp://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210153/20222DOI1 aLIMA, E. de S.  aRandom forest model to predict the height of Eucalyptus.h[electronic resource]  c2022  aSpecial issue: artificial intelligence.  aEucalyptus (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.  aEucalyptus  aExchangeable aluminum  aAlumínio permutável  aAprendizado de máquina  aConteúdo de fósforo no solo  aCrescimento de eucalipto  aEucalyptus urograndis  aFloresta aleatória  aMachine learning  aMistura de solos  aPhysicochemical variables of soil  aSoil moisture  aSoil phosphorus content  aVariáveis físico-químicas do solo1 aSOUZA, Z. M. de1 aOLIVEIRA, S. R. de M.1 aMONTANARI, R.1 aFARHATE, C. V. V.  tEngenharia Agrícolagv. 42, e20210153, 2022.