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dc.contributor.authorANDRADE JUNIOR, A. S. de
dc.contributor.authorSILVA, S. P. da
dc.contributor.authorSETUBAL, I. S.
dc.contributor.authorSOUZA, H. A. de
dc.contributor.authorVIEIRA, P. F. de M. J.
dc.contributor.authorCASARI, R. A. das C. N.
dc.date.accessioned2022-05-19T20:13:42Z-
dc.date.available2022-05-19T20:13:42Z-
dc.date.created2022-05-19
dc.date.issued2022
dc.identifier.citationRevista Brasileira de Engenharia Agrícola e Ambiental, v. 26, n. 6, p. 466-476, 2022.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1143266-
dc.descriptionThis study aimed to evaluate the ability of vegetation indices (VIs) obtained from unmanned aerial vehicle (UAV) images to estimate soybean grain yield under soil and climate conditions in the Teresina microregion, Piaui state (PI), Brazil. Soybean cv. BRS-8980 was evaluated in stage R5 and submitted to two water regimes (WR) (100 and 50% of crop evapotranspiration - ETc) and two N levels (with and without N supplementation).
dc.language.isopor
dc.rightsopenAccess
dc.subjectAeronave remotamente pilotada
dc.subjectÍndices de vegetação
dc.subjectAutocorrelação
dc.titlePredicting soybean grain yield using aerial drone images.
dc.typeArtigo de periódico
dc.subject.thesagroGlycine Max
riaa.ainfo.id1143266
riaa.ainfo.lastupdate2022-05-19
dc.contributor.institutionADERSON SOARES DE ANDRADE JUNIOR, CPAMN; SILVESTRE P. DA SILVA, UFPI; INGRID S. SETUBAL, UFPI; HENRIQUE ANTUNES DE SOUZA, CPAMN; PAULO FERNANDO DE MELO JORGE VIEIRA, CPAMN; RAPHAEL A. DAS C. N. CASARI, CNPAE.
Aparece en las colecciones:Artigo em periódico indexado (CPAMN)

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