Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1160827
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dc.contributor.authorSOUZA, K. X. S. de
dc.contributor.authorTERNES, S.
dc.contributor.authorCAMARGO NETO, J.
dc.contributor.authorSANTOS, T. T.
dc.contributor.authorMOREIRA, A. S.
dc.contributor.authorKOENIGKAN, L. V.
dc.contributor.authorSOUZA, R. de
dc.date.accessioned2024-01-15T15:32:29Z-
dc.date.available2024-01-15T15:32:29Z-
dc.date.created2024-01-15
dc.date.issued2023
dc.identifier.citationIn: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 14., 2023, Natal. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023. p. 262-269.
dc.identifier.issn2177-9724
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1160827-
dc.descriptionIn this paper, we assess the effectiveness of various machine learning regressors for yield forecasting based on fruit detection in images captured within the orchard
dc.language.isoeng
dc.rightsopenAccess
dc.subjectVisão computacional
dc.subjectIdentificação automática de frutas
dc.subjectAutomatic fruit identification
dc.titleEvaluating multiple regressors for the yield of orange orchards.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroLaranja
dc.subject.nalthesaurusOranges
dc.subject.nalthesaurusComputer vision
dc.subject.nalthesaurusImage analysis
dc.description.notesSBIAgro 2023.
riaa.ainfo.id1160827
riaa.ainfo.lastupdate2024-01-15
dc.identifier.doihttps://doi.org/10.5753/sbiagro.2023.26567
dc.contributor.institutionKLEBER XAVIER SAMPAIO DE SOUZA, CNPTIA; SONIA TERNES, CNPTIA; JOAO CAMARGO NETO, CNPTIA; THIAGO TEIXEIRA SANTOS, CNPTIA; ALECIO SOUZA MOREIRA, CNPMF; LUCIANO VIEIRA KOENIGKAN, CNPTIA; ROBERTA DE SOUZA.
Appears in Collections:Artigo em anais de congresso (CNPTIA)

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