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Diago Santamaria, Maria Paz and Correa Farias, Christian and Millan, Borja and Valero Ubierna, Constantino and Barreiro Elorza, Pilar and Tardaguila Laso, Javier (2012). Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions. "Sensors", v. 12 ; pp. 16988-17006. ISSN 1424-8220. https://doi.org/10.3390/s121216988.
Title: | Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions |
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Author/s: |
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Item Type: | Article |
Título de Revista/Publicación: | Sensors |
Date: | 12 December 2012 |
ISSN: | 1424-8220 |
Volume: | 12 |
Subjects: | |
Freetext Keywords: | grapes , vines, vineyard, |
Faculty: | E.T.S.I. Agrónomos (UPM) [antigua denominación] |
Department: | Ingeniería Rural [hasta 2014] |
UPM's Research Group: | LPF-TAGRALIA |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
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The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them. The proposed methodology was evaluated with 70 grapevine (V. vinifera L. cv. Tempranillo) images, acquired in a commercial vineyard located in La Rioja (Spain), after several defoliation and de-fruiting events on 10 vines, with a conventional RGB camera and no artificial illumination. The segmentation results showed a performance of 92% for leaves and 98% for clusters, and allowed to assess the grapevine’s leaf area and yield with R2 values of 0.81 (p < 0.001) and 0.73 (p = 0.002), respectively. This methodology, which operates with a simple image acquisition setup and guarantees the right number and kind of pixel classes, has shown to be suitable and robust enough to provide valuable information for vineyard management.
Item ID: | 14126 |
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DC Identifier: | https://oa.upm.es/14126/ |
OAI Identifier: | oai:oa.upm.es:14126 |
DOI: | 10.3390/s121216988 |
Official URL: | http://www.mdpi.com/1424-8220/12/12/16988 |
Deposited by: | Investigador en formación Christian Correa Farías |
Deposited on: | 13 Dec 2012 09:54 |
Last Modified: | 21 Apr 2016 13:37 |