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 Universidade de Trás-os-Montes e Alto Douro
    info

    Universidade de Trás-os-Montes e Alto Douro

    Vila Real, Portugal

    ROR https://ror.org/03qc8vh97

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

    Instituto de Ciencias de la Vid y del Vino

    Logroño, España

    ROR https://ror.org/01rm2sw78

  3. 3 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revista:
Computers and Electronics in Agriculture

ISSN: 0168-1699

Año de publicación: 2013

Volumen: 99

Páginas: 7-13

Tipo: Artículo

DOI: 10.1016/J.COMPAG.2013.08.021 SCOPUS: 2-s2.0-84884389249 WoS: WOS:000327919700002 GOOGLE SCHOLAR

Otras publicaciones en: Computers and Electronics in Agriculture

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Resumen

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.