English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/144434
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:

Title

Automatic discrimination of grapevine (Vitis vinifera L.) clones using leaf hyperspectral imaging and partial least squares

AuthorsFernandes, Armando M.; Melo-Pinto, Pedro; Millán Prior, Borja ; Tardáguila, Javier ; Diago, Maria P.
Issue DateApr-2015
PublisherCambridge University Press
CitationJournal of Agricultural Science 153(3): 455-465 (2015)
Abstract© Cambridge University Press 2014. A worldwide innovative method to discriminate grapevine clones is presented. It is an alternative to ampelography, isozyme and DNA analysis. The spectra and their first and second derivatives of 201 bands in the visible and near-infrared wavelength range between 634 and 759 nm were used as inputs to a classifier created using partial least squares. The spectra were acquired in the laboratory for the adaxial side of the apical part of the main lobe of fully hydrated grapevine leaves. The classifier created allowed the separation of 100 leaves of the Cabernet Sauvignon (Vitis vinifera L.) variety into four clones, namely CS 15, CS 169, CS 685 and CS R5, comprising 25 leaves each. The percentages of leaves correctly classified for these clones were 98·2, 99·2, 100 and 97·8%, respectively, when the classifier input was the second derivative of the normalized spectra. These percentages were determined by Monte-Carlo cross-validation. With the new method proposed, each leaf of a given variety can be classified in a few seconds according to its clone in an environmentally friendly way.
Publisher version (URL)http://doi.org/10.1017/S0021859614000252
URIhttp://hdl.handle.net/10261/144434
DOIhttp://dx.doi.org/10.1017/S0021859614000252
Identifierse-issn: 1469-5146
issn: 0021-8596
Appears in Collections:(ICVV) Artículos
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf15,38 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.