Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1143293
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dc.contributor.authorOSCO, L. P.
dc.contributor.authorFURUYA, D. E. G.
dc.contributor.authorFURUYA, M. T. G.
dc.contributor.authorCORRÊA, D. V.
dc.contributor.authorGONÇALVEZ, W. N.
dc.contributor.authorMARCATO JUNIOR, J.
dc.contributor.authorBORGES, M.
dc.contributor.authorMORAES, M. C. B.
dc.contributor.authorMICHEREFF, M. F. F.
dc.contributor.authorAQUINO, M. F. S.
dc.contributor.authorLAUMANN, R. A.
dc.contributor.authorLISENBERG, V.
dc.contributor.authorRAMOS, A. P. M.
dc.contributor.authorJORGE, L. A. de C.
dc.date.accessioned2022-07-15T10:20:33Z-
dc.date.available2022-07-15T10:20:33Z-
dc.date.created2022-05-20
dc.date.issued2022
dc.identifier.citationInfrared Physics & Technology, v. 123, 2022. 104203.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1143293-
dc.language.isopor
dc.rightsopenAccess
dc.subjectField spectroscopy
dc.titleAn impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods.
dc.typeArtigo de periódico
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusPrecision agriculture
dc.subject.nalthesaurusArtificial intelligence
dc.description.notesNa publicação: Maria Carolina Blassioli-Moraes.
riaa.ainfo.id1143293
riaa.ainfo.lastupdate2022-07-14
dc.identifier.doihttps://doi.org/10.1016/j.infrared.2022.104203
dc.contributor.institutionLUCAS PRADO OSCO, Unoeste; DANIELLE ELIS GARCIA FURUYA, Unoeste; MICHELLE TAÍS GARCIA FURUYA, Unoeste; DANIEL VERAS CORRÊA, Unoeste; WESLEY NUNES GONÇALVEZ, UFMS; JOSÉ MARCATO JUNIOR, UFMS; MIGUEL BORGES, Cenargen; MARIA CAROLINA BLASSIOLI MORAES, Cenargen; MIRIAN FERNANDES FURTADO MICHEREFF; MICHELY FERREIRA SANTOS AQUINO; RAUL ALBERTO LAUMANN, Cenargen; VERALDO LISENBERG, UDESC; ANA PAULA MARQUES RAMOS, Unoeste; LUCIO ANDRE DE CASTRO JORGE, CNPDIA.
Appears in Collections:Artigo em periódico indexado (CENARGEN)

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