Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1163727
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dc.contributor.authorALMEIDA, I. P.
dc.contributor.authorFERNANDES, A.
dc.contributor.authorPARREIRA, W. D.
dc.contributor.authorOLIVEIRA, M. F. de
dc.contributor.authorGUCKERT, K. S.
dc.contributor.authorCOELHO, D. K.
dc.date.accessioned2024-04-18T07:52:44Z-
dc.date.available2024-04-18T07:52:44Z-
dc.date.created2024-04-17
dc.date.issued2024
dc.identifier.citationIn: COMPUTER ON THE BEACH, 15., 2024, Balneário Camburiú. Anais... São José: Universidade do Vale do Itajaí, 2024.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1163727-
dc.descriptionPasture degradation is a significant challenge in livestock farming in Brazil, affecting the environmental and economic sustainability of the sector. Solutions that help manage pasture areas are crucial for Brazilian agribusiness. In this context, this work presents the application of YOLO model of Deep Learning to identify exposed soil, as well as indicators of pasture degradation, in this case, the number of termite mounds in each area. The image base used refers to pastures in Goiás and Mato Grosso.
dc.language.isopor
dc.rightsopenAccess
dc.subjectDegradação de pastagem
dc.subjectModelo YOLO
dc.subjectDeep Learning
dc.titleIdentificação de solo exposto e cupinzeiros em pastagens utilizando deep learning.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroPastagem
dc.subject.thesagroCupim
dc.subject.nalthesaurusDegradation
dc.subject.nalthesaurusPastures
riaa.ainfo.id1163727
riaa.ainfo.lastupdate2024-04-17
dc.contributor.institutionIAN PINTO ALMEIDA, UNIVERSIDADE DO VALE DO ITAJAÍ; ANITA FERNANDES, UNIVERSIDADE DO VALE DO ITAJAÍ; WEMERSON DELCIO PARREIRA, PUC-CAMPINAS; MAURILIO FERNANDES DE OLIVEIRA, CNPMS; KAROLINE SOUZA GUCKERT, UNIVERSIDADE DO VALE DO ITAJAÍ; DENNIS KERR COELHO, UNIVERSIDADE DO VALE DO ITAJAÍ.
Appears in Collections:Artigo em anais de congresso (CNPMS)

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