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dc.contributor.authorLIMA, E. de S.
dc.contributor.authorSOUZA, Z. M. de
dc.contributor.authorOLIVEIRA, S. R. de M.
dc.contributor.authorMONTANARI, R.
dc.contributor.authorFARHATE, C. V. V.
dc.date.accessioned2022-04-06T12:05:44Z-
dc.date.available2022-04-06T12:05:44Z-
dc.date.created2022-04-06
dc.date.issued2022
dc.identifier.citationEngenharia Agrícola, v. 42, e20210153, 2022.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1141899-
dc.descriptionEucalyptus (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.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectVariáveis físico-químicas do solo
dc.subjectAprendizado de máquina
dc.subjectConteúdo de fósforo no solo
dc.subjectMistura de solos
dc.subjectAlumínio permutável
dc.subjectEucalyptus urograndis
dc.subjectFloresta aleatória
dc.subjectCrescimento de eucalipto
dc.subjectPhysicochemical variables of soil
dc.subjectMachine learning
dc.subjectSoil phosphorus content
dc.subjectSoil moisture
dc.titleRandom forest model to predict the height of Eucalyptus.
dc.typeArtigo de periódico
dc.subject.nalthesaurusEucalyptus
dc.subject.nalthesaurusExchangeable aluminum
dc.description.notesSpecial issue: artificial intelligence.
riaa.ainfo.id1141899
riaa.ainfo.lastupdate2022-04-06
dc.identifier.doihttp://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210153/2022
dc.contributor.institutionELIZEU 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.
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