Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions

Diago Santamaria, Maria Paz; Correa Farias, Christian; Millan, Borja; Valero Ubierna, Constantino; Barreiro Elorza, Pilar y 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.

Descripción

Título: Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions
Autor/es:
  • Diago Santamaria, Maria Paz
  • Correa Farias, Christian
  • Millan, Borja
  • Valero Ubierna, Constantino
  • Barreiro Elorza, Pilar
  • Tardaguila Laso, Javier
Tipo de Documento: Artículo
Título de Revista/Publicación: Sensors
Fecha: 12 Diciembre 2012
Volumen: 12
Materias:
Palabras Clave Informales: grapes , vines, vineyard,
Escuela: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Departamento: Ingeniería Rural [hasta 2014]
Grupo Investigación UPM: LPF-TAGRALIA
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 14126
Identificador DC: http://oa.upm.es/14126/
Identificador OAI: oai:oa.upm.es:14126
Identificador DOI: 10.3390/s121216988
URL Oficial: http://www.mdpi.com/1424-8220/12/12/16988
Depositado por: Investigador en formación Christian Correa Farías
Depositado el: 13 Dic 2012 09:54
Ultima Modificación: 21 Abr 2016 13:37
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