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dc.contributor.authorFernández, Roemi
dc.contributor.authorMontes Franceschi, Héctor
dc.contributor.authorSalinas, Carlota
dc.contributor.authorSarria, Javier
dc.contributor.authorArmada, Manuel
dc.date.accessioned2017-07-31T16:00:04Z
dc.date.available2017-07-31T16:00:04Z
dc.date.issued06/19/2013
dc.identifierhttp://www.mdpi.com/1424-8220/13/6/7838
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/2363
dc.descriptionThis paper proposes a sequential masking algorithm based on the K-means method that combines RGB and multispectral imagery for discrimination of Cabernet Sauvignon grapevine elements in unstructured natural environments, without placing any screen behind the canopy and without any previous preparation of the vineyard. In this way, image pixels are classified into five clusters corresponding to leaves, stems, branches, fruit and background. A custom-made sensory rig that integrates a CCD camera and a servo-controlled filter wheel has been specially designed and manufactured for the acquisition of images during the experimental stage. The proposed algorithm is extremely simple, efficient, and provides a satisfactory rate of classification success. All these features turn out the proposed algorithm into an appropriate candidate to be employed in numerous tasks of the precision viticulture, such as yield estimation, water and nutrients needs estimation, spraying and harvesting.en_US
dc.description.abstractThis paper proposes a sequential masking algorithm based on the K-means method that combines RGB and multispectral imagery for discrimination of Cabernet Sauvignon grapevine elements in unstructured natural environments, without placing any screen behind the canopy and without any previous preparation of the vineyard. In this way, image pixels are classified into five clusters corresponding to leaves, stems, branches, fruit and background. A custom-made sensory rig that integrates a CCD camera and a servo-controlled filter wheel has been specially designed and manufactured for the acquisition of images during the experimental stage. The proposed algorithm is extremely simple, efficient, and provides a satisfactory rate of classification success. All these features turn out the proposed algorithm into an appropriate candidate to be employed in numerous tasks of the precision viticulture, such as yield estimation, water and nutrients needs estimation, spraying and harvesting.en_US
dc.language.isoenen_US
dc.rightshttp://www.creativecommons.org/licenses/by-nc/3.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectmultispectral imageryen_US
dc.subjectprecision viticultureen_US
dc.subjectCabernet Sauvignonen_US
dc.subjectoptical filtersen_US
dc.subjectimage processingen_US
dc.subjectclassificationen_US
dc.subjectK-meansen_US
dc.titleCombination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elementsen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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