Identification of grapevine varieties using leaf spectroscopy and partial least squares

  1. Diago, M.P. 2
  2. Fernandes, A.M. 1
  3. Millan, B. 2
  4. Tardaguila, J. 23
  5. Melo-Pinto, P. 1
  1. 1 University of Trás-os-Montes and Alto Douro
    info

    University of Trás-os-Montes and Alto Douro

    Vila Real, Portugal

    GRID grid.12341.35

  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:
Computers and Electronics in Agriculture

ISSN: 0168-1699

Year of publication: 2013

Volume: 99

Pages: 7-13

Type: Article

Export: RIS
DOI: 10.1016/j.compag.2013.08.021 SCOPUS: 2-s2.0-84884389249 WoS: 000327919700002 GOOGLE SCHOLAR

Metrics

Cited by

  • Scopus Cited by: 42 (14-07-2021)

Journal Citation Reports

  • Year 2013
  • Journal Impact Factor: 1.486
  • Best Quartile: Q1
  • Area: AGRICULTURE, MULTIDISCIPLINARY Quartile: Q1 Rank in area: 10/56 (Ranking edition: SCIE)
  • Area: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Quartile: Q2 Rank in area: 49/102 (Ranking edition: SCIE)

SCImago Journal Rank

  • Year 2013
  • SJR Journal Impact: 0.95
  • Best Quartile: Q1
  • Area: Agronomy and Crop Science Quartile: Q1 Rank in area: 45/345
  • Area: Animal Science and Zoology Quartile: Q1 Rank in area: 46/400
  • Area: Computer Science Applications Quartile: Q1 Rank in area: 118/1477
  • Area: Forestry Quartile: Q1 Rank in area: 15/158
  • Area: Horticulture Quartile: Q1 Rank in area: 9/76

CiteScore

  • Year 2013
  • CiteScore of the Journal : 4.5
  • Area: Animal Science and Zoology Percentile: 95
  • Area: Forestry Percentile: 95
  • Area: Horticulture Percentile: 94
  • Area: Agronomy and Crop Science Percentile: 90
  • Area: Computer Science Applications Percentile: 82

Related Projects

Abstract

Grapevine variety identification is a matter of great interest in viticulture, which is currently addressed by visual ampelometry or wet chemistry genetic analysis. This paper reports the development of a simple and automatic method of classification of grapevine varieties from leaf spectroscopy. The method consists of a classifier based on partial least squares that discriminates among grapevine varieties using a hyperspectral image of a leaf measured in reflectance mode. Hyperspectral imaging was conducted with a camera with 1040 wavelength bands operating between 380. nm and 1028. nm. The classifier was created using 300 leaves, 100 of each of the varieties Vitis vinifera L., Tempranillo, Grenache and Cabernet Sauvignon. Monte-Carlo cross-validation confirmed the classifier's performance for the three varieties, which exceeded 92% in all cases. The proposed method has proven to satisfactory classify among grape varieties, but certainly a wider range of grapevine cultivars should be tested before it gets implemented for local sensing with the aim of providing the wine industry with a fast, automatic, environmentally friendly and accurate tool for grapevine variety identification. © 2013 Elsevier B.V.