Índices de vegetação obtidos por sensor proximal e embarcado em aeronave remotamente pilotada e sua relação com a produtividade do milho

• Ano de defesa: 2019
• Autor(a) principal: Carvalho, Luiz Felipe Diaz de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Agronomia; UFSM; Programa de Pós-Graduação em Ciência do Solo; Centro de Ciências Rurais
• Palavras-chave em Português: Agricultura de precisão; Nitrogênio; Sensoriamento remoto; Sistema de aeronave remotamente pilotada; Precision agriculture; Nitrogen; Remote sensing; Remotely piloted aircraft system; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
• Link de acesso: http://repositorio.ufsm.br/handle/1/18963

Different vegetation indices (IVs) have been used as a tool to evaluate plant biophysical parameters. Among these, we highlight the success of NDVI in the evaluation of nitrogen nutritional status (N) in maize, wheat, barley, among others. The objective of the study was to evaluate the relationship between IVs determined by optical sensors, boarded on two different platforms, the proximal, and the Remotely Piloted Aircraft System (SARP). Quantifications were carried out at several corn phenological stages, under cultivation submitted to different N nutritional conditions. At the time of maize sowing, N doses of 20, 60, 120, 180 and 240 kg ha-1 were applied, arranged in a randomized block design, with 5 replicates each. The IVs investigated were NDRE and NDVI (proximal platform), and NDRE, NDVI, EVI2 and GNDVI (in SARP platform), evaluated in the phenological stages V5, V6, V7, V9, V11 and V12. For a population of 6400 plants evaluated by platforms, the spatial resolution obtained for the embedded platform was 3.7 cm / px-1 in the monochromatic and 0.8 cm / px-1 in the RGB mode. The nutritional status of the corn was monitored by evaluating the N content in the aerial part of the plant and N absorbed at harvest. In the first article a relation of the vegetation indices with the nutritional state of the plants was carried out and comparing the proximal sensors with embedded in SARP. In the second article, correlations of vegetation indices with corn yield were carried out.