Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements

This 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 preparati...

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Main Authors:	Fernández, Roemi, Montes, Héctor, Salinas, Carlota, Sarria, Javier, Armada, Manuel
Format:	Online
Language:	English
Published:	Molecular Diversity Preservation International (MDPI) 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3715248/

id	pubmed-3715248
recordtype	oai_dc
spelling	pubmed-37152482013-07-24 Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements Fernández, Roemi Montes, Héctor Salinas, Carlota Sarria, Javier Armada, Manuel Article This 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. Molecular Diversity Preservation International (MDPI) 2013-06-19 /pmc/articles/PMC3715248/ /pubmed/23783736 http://dx.doi.org/10.3390/s130607838 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Fernández, Roemi Montes, Héctor Salinas, Carlota Sarria, Javier Armada, Manuel
spellingShingle	Fernández, Roemi Montes, Héctor Salinas, Carlota Sarria, Javier Armada, Manuel Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements
author_facet	Fernández, Roemi Montes, Héctor Salinas, Carlota Sarria, Javier Armada, Manuel
author_sort	Fernández, Roemi
title	Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements
title_short	Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements
title_full	Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements
title_fullStr	Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements
title_full_unstemmed	Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements
title_sort	combination of rgb and multispectral imagery for discrimination of cabernet sauvignon grapevine elements
description	This 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.
publisher	Molecular Diversity Preservation International (MDPI)
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3715248/
_version_	1611995901271736320

Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements

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