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dc.creatorSilva, Jesus
dc.creatorVarela, Noel
dc.creatorDíaz-Martinez, Jorge L.
dc.creatorJiménez-Cabas, Javier
dc.creatorPineda Lezama, Omar Bonerge
dc.date.accessioned2020-08-14T14:49:18Z
dc.date.available2020-08-14T14:49:18Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/6930
dc.description.abstractBeekeeping 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.spa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceAdvances in Intelligent Systems and Computingspa
dc.subjectClassification of polliniferous vegetationspa
dc.subjectMultispectral imagingspa
dc.subjectNeural networksspa
dc.titleApproach for the classification of polliniferous vegetation using multispectral imaging and neural networksspa
dc.typearticlespa
dc.source.urlhttps://link.springer.com/chapter/10.1007%2F978-3-030-51859-2_24spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1007/978-3-030-51859-2_24
dc.type.hasversioninfo:eu-repo/semantics/draftspa


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CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal