Approach for the classification of polliniferous vegetation using multispectral imaging and neural networks
Beekeeping has suffered a serious deterioration in the regions of the world. The possibility of nectar-polliniferous resources has decreased and, therefore, the feeding of bees, with the consequent decrease in production. There is, therefore, a need to improve marketing and diversification systems a...
- Autores:
-
Silva, Jesus
Varela, Noel
Díaz-Martinez, Jorge L.
Jiménez-Cabas, Javier
Pineda Lezama, Omar Bonerge
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/6930
- Acceso en línea:
- https://hdl.handle.net/11323/6930
https://doi.org/10.1007/978-3-030-51859-2_24
https://repositorio.cuc.edu.co/
- Palabra clave:
- Classification of polliniferous vegetation
Multispectral imaging
Neural networks
- Rights
- openAccess
- License
- CC0 1.0 Universal
| Summary: | Beekeeping has suffered a serious deterioration in the regions of the world. The possibility of nectar-polliniferous resources has decreased and, therefore, the feeding of bees, with the consequent decrease in production. There is, therefore, a need to improve marketing and diversification systems and to update production techniques and the management of the production process by beekeepers to obtain the quality of honey required by the market [1]. This work proposes the use of spectral information to identify the different pollen-producing plants using remote vision, image processing, and artificial neural networks. |
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