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 submitte...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Brasil |
| Institución: | Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
| Repositorio: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
| Idioma: | portugués |
| OAI Identifier: | oai:www.alice.cnptia.embrapa.br:doc/1143266 |
| Acceso en línea: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1143266 |
| Access Level: | acceso abierto |
| Palabra clave: | Aeronave remotamente pilotada Índices de vegetação Autocorrelação Glycine Max |
| Sumario: | 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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