Using artificial intelligence (AI) for grapevine disease detection based on images

  1. Poblete-Echeverría, Carlos 12
  2. Hernández, Inés 12
  3. Gutiérrez, Salvador 3
  4. Iñiguez, Rubén 12
  5. Barrio, Ignacio 12
  6. Tardaguila, Javier 12
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  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 Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

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Konferenzberichte:
BIO Web of Conferences: 44th World Congress of Vine and Wine
  1. Pau Roca (ed. lit.)

Verlag: Web of Conferences

ISSN: 2117-4458

Datum der Publikation: 2023

Ausgabe: 68

Kongress: 44th World Congress of Vine and Wine (44º. 2023. Cádiz/Jerez)

Art: Konferenz-Beitrag

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DOI: 10.1051/BIOCONF/20236801021 GOOGLE SCHOLAR lock_openOpen Access editor

Zusammenfassung

Nowadays, diseases are one of the major threats to sustainable viticulture. Manual detection through visual surveys, usually done by agronomists, relies on symptom identification and requires an enormous amount of time. Detection in field conditions remains difficult due to the lack of infrastructure to perform detailed and rapid field scouting covering the whole vineyard. In general, symptoms of grapevine diseases can be seen as spots and patterns on leaves. In this sense, computer vision technologies and artificial intelligence (AI) provide an excellent alternative to improve the current disease detection and quantification techniques using images of leaves and canopy. These novel methods can minimize the time spent on symptom detection, which helps in the control and quantification of the disease severity. In this article, we present some results of deep learning-based approaches used for detecting automatically leaves with downy mildew symptoms from RGB images acquired under laboratory and field conditions. The results obtained so far with AI approaches for detecting leaves with downy mildew symptoms are promising, and they put in evidence of the huge potential of these techniques for practical applications in the context of modern and sustainable viticulture.

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