Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1121648
Full metadata record
DC FieldValueLanguage
dc.contributor.authorOSCO, L. P.
dc.contributor.authorRAMOS, A. P. M.
dc.contributor.authorPINHEIRO, M. M. F.
dc.contributor.authorMORIYA, E. A. S.
dc.contributor.authorIMAI, N. N.
dc.contributor.authorESTRABIS, N.
dc.contributor.authorIANCZYK, F.
dc.contributor.authorARAÚJO, F. F.
dc.contributor.authorLIESENBERG, V.
dc.contributor.authorJORGE, L. A. de C.
dc.contributor.authorLI, J.
dc.contributor.authorMA, L.
dc.contributor.authorGONÇALVES, W. N.
dc.contributor.authorMARCATO JUNIOR, J.
dc.contributor.authorCRESTE, J. E.
dc.date.accessioned2022-04-08T11:02:20Z-
dc.date.available2022-04-08T11:02:20Z-
dc.date.created2020-04-14
dc.date.issued2020
dc.identifier.citationRemote Sensing, n. 12, v. 6, a. 906, 2020.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1121648-
dc.languageIngles
dc.language.isoen
dc.rightsopenAccess
dc.subjectProximal sensor
dc.subjectMacronutrient
dc.subjectMicronutrient
dc.titleA machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements.
dc.typeArtigo de periódico
dc.format.extent21 - 21
riaa.ainfo.id1121648
riaa.ainfo.lastupdate2022-04-07
dc.identifier.doi10.3390/rs12060906
dc.contributor.institutionLUCIO ANDRE DE CASTRO JORGE, CNPDIA.
Appears in Collections:Artigo em periódico indexado (CNPDIA)

Files in This Item:
File Description SizeFormat 
P-A-Machine-Learning-Framework-to-Predict-Nutrient-....pdf4,7 MBAdobe PDFThumbnail
View/Open

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace