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

Journal:
Sensors

ISSN: 1424-8220

Year of publication: 2012

Volume: 12

Issue: 12

Pages: 16988-17006

Type: Article

Export: RIS
DOI: 10.3390/s121216988 SCOPUS: 2-s2.0-84871686486 WoS: 000312607500056 GOOGLE SCHOLAR lock_openOpen access editor
Institutional archive: lock_openOpen access editor

Metrics

Cited by

  • Scopus Cited by: 98 (12-06-2021)

Journal Citation Reports

  • Year 2012
  • Journal Impact Factor: 1.953
  • Best Quartile: Q1
  • Area: INSTRUMENTS & INSTRUMENTATION Quartile: Q1 Rank in area: 8/57 (Ranking edition: SCIE)
  • Area: CHEMISTRY, ANALYTICAL Quartile: Q3 Rank in area: 38/75 (Ranking edition: SCIE)
  • Area: ELECTROCHEMISTRY Quartile: Q3 Rank in area: 15/26 (Ranking edition: SCIE)

SCImago Journal Rank

  • Year 2012
  • SJR Journal Impact: 0.671
  • Best Quartile: Q1
  • Area: Information Systems Quartile: - Rank in area: 91/1188
  • Area: Electrical and Electronic Engineering Quartile: Q1 Rank in area: 165/2153
  • Area: Instrumentation Quartile: Q1 Rank in area: 26/141
  • Area: Analytical Chemistry Quartile: Q2 Rank in area: 44/107
  • Area: Atomic and Molecular Physics, and Optics Quartile: Q2 Rank in area: 57/268
  • Area: Medicine (miscellaneous) Quartile: Q2 Rank in area: 899/2943
  • Area: Biochemistry Quartile: Q3 Rank in area: 222/407

CiteScore

  • Year 2012
  • CiteScore: 3.5
  • Area: Electrical and Electronic Engineering Percentile: 82
  • Area: Atomic and Molecular Physics, and Optics Percentile: 79
  • Area: Analytical Chemistry Percentile: 60
  • Area: Biochemistry Percentile: 49

Summary

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.