Predicting soybean grain yield using aerial drone images.

Predicting soybean grain yield using aerial drone images.

This 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).

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Bibliographic Details
Main Authors: ANDRADE JUNIOR, A. S. de, SILVA, S. P. da, SETUBAL, I. S., SOUZA, H. A. de, VIEIRA, P. F. de M. J., CASARI, R. A. das C. N.
Other Authors: ADERSON 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.
Format: Artigo de periódico biblioteca
Language:Portugues
pt_BR
Published: 2022-05-19
Subjects:Aeronave remotamente pilotada, Índices de vegetação, Autocorrelação, Glycine Max,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/1143266
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Summary:This 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).

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