Grapevine yield and leaf area estimation using supervised classification methodology on RGB images taken under field conditions

  1. Diago, M.-P. 2
  2. Correa, C. 1
  3. Millán, B. 2
  4. Barreiro, P. 1
  5. Valero, C. 1
  6. Tardaguila, J. 23
  1. 1 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    GRID grid.5690.a

  2. 2 Instituto de Ciencias de la Vid y del Vino
    info

    Instituto de Ciencias de la Vid y del Vino

    Logroño, España

    GRID grid.481584.4

  3. 3 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    GRID grid.119021.a

Revista:
Sensors

ISSN: 1424-8220

Año de publicación: 2012

Volumen: 12

Número: 12

Páginas: 16988-17006

Tipo: Artículo

Exportar: RIS
DOI: 10.3390/s121216988 SCOPUS: 2-s2.0-84871686486 WoS: 000312607500056 GOOGLE SCHOLAR lock_openAcceso abierto editor
Archivo institucional: lock_openAcceso abierto editor

Indicadores

Citas recibidas

  • Citas en Scopus: 98 (14-07-2021)

Journal Citation Reports

  • Año 2012
  • Factor de impacto de la revista: 1.953
  • Cuartil mayor: Q1
  • Área: INSTRUMENTS & INSTRUMENTATION Cuartil: Q1 Posición en el área: 8/57 (Edicion: SCIE)
  • Área: CHEMISTRY, ANALYTICAL Cuartil: Q3 Posición en el área: 38/75 (Edicion: SCIE)
  • Área: ELECTROCHEMISTRY Cuartil: Q3 Posición en el área: 15/26 (Edicion: SCIE)

SCImago Journal Rank

  • Año 2012
  • Impacto SJR de la revista: 0.671
  • Cuartil mayor: Q1
  • Área: Information Systems Cuartil: - Posición en el área: 91/1188
  • Área: Electrical and Electronic Engineering Cuartil: Q1 Posición en el área: 165/2153
  • Área: Instrumentation Cuartil: Q1 Posición en el área: 26/141
  • Área: Analytical Chemistry Cuartil: Q2 Posición en el área: 44/107
  • Área: Atomic and Molecular Physics, and Optics Cuartil: Q2 Posición en el área: 57/268
  • Área: Medicine (miscellaneous) Cuartil: Q2 Posición en el área: 899/2943
  • Área: Biochemistry Cuartil: Q3 Posición en el área: 222/407

CiteScore

  • Año 2012
  • CiteScore de la revista: 3.5
  • Área: Electrical and Electronic Engineering Percentil: 82
  • Área: Atomic and Molecular Physics, and Optics Percentil: 79
  • Área: Analytical Chemistry Percentil: 60
  • Área: Biochemistry Percentil: 49

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. © 2012 by the authors; licensee MDPI, Basel, Switzerland.