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Research Article

Automatic Flower Number Evaluation in Grapevine Inflorescences Using RGB Images

Javier Tello, Katja Herzog, Florian Rist, Patrice This, Agnès Doligez
Am J Enol Vitic. January 2020 71: 10-16; published ahead of print September 13, 2019 ; DOI: 10.5344/ajev.2019.19036
Javier Tello
1UMR AGAP, University of Montpellier-CIRAD-INRA-Montpellier SupAgro, Montpellier, France
2UMT Geno-Vigne® IFV-INRA-Montpellier SupAgro, Montpellier, France
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  • For correspondence: javier.tello-moro@inra.fr
Katja Herzog
3Julius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany.
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Florian Rist
3Julius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany.
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Patrice This
1UMR AGAP, University of Montpellier-CIRAD-INRA-Montpellier SupAgro, Montpellier, France
2UMT Geno-Vigne® IFV-INRA-Montpellier SupAgro, Montpellier, France
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Agnès Doligez
1UMR AGAP, University of Montpellier-CIRAD-INRA-Montpellier SupAgro, Montpellier, France
2UMT Geno-Vigne® IFV-INRA-Montpellier SupAgro, Montpellier, France
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Abstract

A precise count of flower number per inflorescence is essential to characterize the reproductive behavior of a vine. Previous efforts to automatize this process by image-based technologies have failed in the development of a universal system that can be applied to multiple grapevine cultivars, or they have been tested in a set of inflorescences of narrow morphological diversity. Here, we have developed an alternative general method in the open-source platform Fiji for the nondestructive counting of visible flowers in red-green-blue (RGB) images, considering inflorescences from 45 different grapevine genotypes from three progenies segregating for inflorescence morphology. The algorithm, based on the segmentation of the image into regions of interest according to their color and morphology, provided counting results highly correlated to manual ones (R2 = 0.91). Similar results were obtained when validating this tool in an external data set of 400 images of four grapevine cultivars. Counting values were used for actual flower number estimation by linear modeling using a subset of 45 images, considering a flower density factor to reduce the adverse effect of the variable number of hidden flowers. Our approach allowed the estimation of flower number with satisfactory results, providing useful information for grapevine breeding and research.

  • computer vision
  • flowering
  • image processing
  • modeling
  • segregating population
  • Vitis vinifera L
  • Received May 2019.
  • Revision received July 2019.
  • Accepted August 2019.
  • ©2020 by the American Society for Enology and Viticulture
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Vol 71 Issue 1

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Automatic Flower Number Evaluation in Grapevine Inflorescences Using RGB Images
Javier Tello, Katja Herzog, Florian Rist, Patrice This, Agnès Doligez
Am J Enol Vitic.  January 2020  71: 10-16;  published ahead of print September 13, 2019 ; DOI: 10.5344/ajev.2019.19036

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Automatic Flower Number Evaluation in Grapevine Inflorescences Using RGB Images
Javier Tello, Katja Herzog, Florian Rist, Patrice This, Agnès Doligez
Am J Enol Vitic.  January 2020  71: 10-16;  published ahead of print September 13, 2019 ; DOI: 10.5344/ajev.2019.19036
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