Detalhes bibliográficos
| Ano de defesa: |
2017 |
| Autor(a) principal: |
Lacerda, Victor Schnepper
 |
| Orientador(a): |
Canteri, Marcelo
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| Banca de defesa: |
Gonçalves, José Eduardo
,
Pria, Maristella Dalla
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| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
UNIVERSIDADE ESTADUAL DE PONTA GROSSA
|
| Programa de Pós-Graduação: |
Programa de Pós Graduação Computação Aplicada
|
| Departamento: |
Computação para Tecnologias em Agricultura
|
| País: |
BR
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://tede2.uepg.br/jspui/handle/prefix/142
|
Resumo: |
Soybean cultivation is of great importance to the Brazilian economy, and one of the major obstacles to its high productivity is the Asian soybean rust, a disease caused by the fungus Phakopsora pachyrhizi. The main measure to control the damage caused by this disease is the application of fungicides at the appropriate time, but the biggest obstacle to its implementation is the difficult detection of Asian rust in its early stages. In this sense, remote sensing combined with the use of unmanned aerial vehicles (UAVs) has potential for disease detection, especially for providing information that is hard to assess by traditional means, and for the advantages of quality and cost of this technology. The present work explores the use of unmanned aerial vehicles to detect and predict the severity of Asian soybean rust by use of digital image processing and data mining techniques for retrieval of predictive models of severity in different development stages. The models obtained showed satisfactory potential for Asian rust detection, and a high correlation between disease severity and the visible spectrum (RGB camera), as it was possible to obtain correlation coefficients greater than 93% after the R5 development stage of the soybean crop. |
