Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1179561
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFRONTADO, N. E. V.eng
dc.contributor.authorDIFANTE, G. dos S.eng
dc.contributor.authorARAUJO, A. R. deeng
dc.contributor.authorMONTAGNER, D. B.eng
dc.contributor.authorSILVA, H. R. daeng
dc.contributor.authorTEODORO, L. P. R.eng
dc.date.accessioned2025-10-09T19:57:17Z-
dc.date.available2025-10-09T19:57:17Z-
dc.date.created2025-10-09
dc.date.issued2024
dc.identifier.citationIn: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 58., 2024, Cuiabá. Zootecnia para segurança alimentar e sustentabilidade climática: anais. Brasília, DF: SBZ; Cuiabá: Universidade Federal de Mato Grosso, 2024.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1179561-
dc.descriptionForage plants of the species Megathyrsus maximus (Syn. Panicum maximum) are an important alternative for the more than 160 million hectares devoted to meat production on pasture. Train and validate machine learning algorithms to identify the most accurate model in classifying cultivars and genotypes of this species. The objective was to evaluate the performance of two classification models low-growing M. maximus forages using dry mass production data and pasture structural and morphogenic variables as inputs.
dc.language.isoeng
dc.rightsopenAccess
dc.titleUsing Machine Learning to classify low-growing forage plants of Megathyrsus maximus (Syn. Panicum maximum).
dc.typeResumo em anais e proceedings
dc.subject.thesagroGenótipoeng
dc.subject.thesagroPanicum Maximumeng
dc.subject.nalthesaurusForage
dc.subject.nalthesaurusGenotype
dc.subject.nalthesaurusMegathyrsus maximus
dc.subject.nalthesaurusMorphogenesiseng
dc.subject.nalthesaurusPastureseng
riaa.ainfo.id1179561
riaa.ainfo.lastupdate2025-10-09
dc.contributor.institutionNÉSTOR EDUARDO VILLAMIZAR FRONTADO, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SULeng
dc.contributor.institutionGELSON DOS SANTOS DIFANTE, UNIVERSIDADE DE MATO GROSSO DO SULeng
dc.contributor.institutionALEXANDRE ROMEIRO DE ARAUJO, CNPGCeng
dc.contributor.institutionDENISE BAPTAGLIN MONTAGNER, CNPGCeng
dc.contributor.institutionHITALO RODRIGUES DA SILVA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SULeng
dc.contributor.institutionLARISSA PEREIRA RIBEIRO TEODORO, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL.eng
Appears in Collections:Resumo em anais de congresso (CNPGC)

Files in This Item:
File Description SizeFormat 
Using-machine-learning-2024.pdf252,27 kBAdobe PDFThumbnail
View/Open
Show simple item record

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace