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
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| dc.title.spa.fl_str_mv |
Approach for the classification of polliniferous vegetation using multispectral imaging and neural networks |
| title |
Approach for the classification of polliniferous vegetation using multispectral imaging and neural networks |
| spellingShingle |
Approach for the classification of polliniferous vegetation using multispectral imaging and neural networks Classification of polliniferous vegetation Multispectral imaging Neural networks |
| title_short |
Approach for the classification of polliniferous vegetation using multispectral imaging and neural networks |
| title_full |
Approach for the classification of polliniferous vegetation using multispectral imaging and neural networks |
| title_fullStr |
Approach for the classification of polliniferous vegetation using multispectral imaging and neural networks |
| title_full_unstemmed |
Approach for the classification of polliniferous vegetation using multispectral imaging and neural networks |
| title_sort |
Approach for the classification of polliniferous vegetation using multispectral imaging and neural networks |
| dc.creator.fl_str_mv |
Silva, Jesus Varela, Noel Díaz-Martinez, Jorge L. Jiménez-Cabas, Javier Pineda Lezama, Omar Bonerge |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesus Varela, Noel Díaz-Martinez, Jorge L. Jiménez-Cabas, Javier Pineda Lezama, Omar Bonerge |
| dc.subject.spa.fl_str_mv |
Classification of polliniferous vegetation Multispectral imaging Neural networks |
| topic |
Classification of polliniferous vegetation Multispectral imaging Neural networks |
| description |
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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2020 |
| dc.date.accessioned.none.fl_str_mv |
2020-08-14T14:49:18Z |
| dc.date.available.none.fl_str_mv |
2020-08-14T14:49:18Z |
| dc.date.issued.none.fl_str_mv |
2020 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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https://hdl.handle.net/11323/6930 |
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https://doi.org/10.1007/978-3-030-51859-2_24 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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https://hdl.handle.net/11323/6930 https://doi.org/10.1007/978-3-030-51859-2_24 https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| dc.language.iso.none.fl_str_mv |
eng |
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eng |
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CC0 1.0 Universal |
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http://creativecommons.org/publicdomain/zero/1.0/ |
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CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
| dc.publisher.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.source.spa.fl_str_mv |
Advances in Intelligent Systems and Computing |
| institution |
Corporación Universidad de la Costa |
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https://link.springer.com/chapter/10.1007%2F978-3-030-51859-2_24 |
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Silva, JesusVarela, NoelDíaz-Martinez, Jorge L.Jiménez-Cabas, JavierPineda Lezama, Omar Bonerge2020-08-14T14:49:18Z2020-08-14T14:49:18Z2020https://hdl.handle.net/11323/6930https://doi.org/10.1007/978-3-030-51859-2_24Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Silva, JesusVarela, NoelDíaz-Martinez, Jorge L.Jiménez-Cabas, Javier-will be generated-orcid-0000-0001-9707-8418-600Pineda Lezama, Omar BonergeengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007%2F978-3-030-51859-2_24Classification of polliniferous vegetationMultispectral imagingNeural networksApproach for the classification of polliniferous vegetation using multispectral imaging and neural networksArtículo de 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