Evaluation of a NIR camera for monitoring yield and nitrogen effect in sugarcane
The main objective of the research carried out in the sugar productive sector in Colombia is to improve crop productivity of sugarcane. The rise of RPAS, together with the use of multispectral cameras, which allows for high spatial resolution images and spectral information outside the visible spect...
- Autores:
-
García, Cesar Edwin
Montero, David
Chica, Hector Alberto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2017
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/61396
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/61396
http://bdigital.unal.edu.co/60206/
- Palabra clave:
- 63 Agricultura y tecnologías relacionadas / Agriculture
nitrogen
yield
near infrared
precision agriculture
vegetation index
RPAS.
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
Summary: | The main objective of the research carried out in the sugar productive sector in Colombia is to improve crop productivity of sugarcane. The rise of RPAS, together with the use of multispectral cameras, which allows for high spatial resolution images and spectral information outside the visible spectrum, has generated an alternative nondestructive technological approach to monitoring crop sugarcane that must be evaluated and adapted to the specific conditions of Colombia´s sugar productive sector. In this context, this paper assesses the potential of a modified camera (NIR) to discriminate three varieties of sugarcane, as well as three doses of fertilization and estimating the sugarcane yield at an early stage, for the three varieties through multiple vegetation indices. In this study, no significant differences were found by vegetation index between fertilization doses, and only significant differences between varieties were found when the fertilization was normal or high. Likewise, multiple regressions between scores derived from vegetation indices after applying PCA and productivity produced determinations of up to 56%. |
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