Assessment of cluster yield components by image analysis

  1. Diago, M.P. 1
  2. Tardaguila, J. 1
  3. Aleixos, N. 2
  4. Millan, B. 1
  5. Prats-Montalban, J.M. 2
  6. Cubero, S. 3
  7. Blasco, J. 3
  1. 1 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

  2. 2 Universidad Politécnica de Valencia
    info

    Universidad Politécnica de Valencia

    Valencia, España

    ROR https://ror.org/01460j859

  3. 3 Instituto Valenciano de Investigaciones Agrarias
    info

    Instituto Valenciano de Investigaciones Agrarias

    Moncada i Reixac, España

    ROR https://ror.org/00kx3fw88

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Revista:
Journal of the Science of Food and Agriculture

ISSN: 0022-5142

Año de publicación: 2015

Volumen: 95

Número: 6

Páginas: 1274-1282

Tipo: Artículo

DOI: 10.1002/JSFA.6819 SCOPUS: 2-s2.0-84924902281 WoS: WOS:000351394200017 GOOGLE SCHOLAR

Otras publicaciones en: Journal of the Science of Food and Agriculture

Resumen

Berry weight, berry number and cluster weight are key parameters for yield estimation for wine and tablegrape industry. Current yield prediction methods are destructive, labour-demanding and time-consuming. In this work, a new methodology, based on image analysis was developed to determine cluster yield components in a fast and inexpensive way. RESULTS: Clusters of seven different red varieties of grapevine (Vitis vinifera L.) were photographed under laboratory conditions and their cluster yield components manually determined after image acquisition. Two algorithms based on the Canny and the logarithmic image processing approaches were tested to find the contours of the berries in the images prior to berry detection performed by means of the Hough Transform. Results were obtained in two ways: by analysing either a single image of the cluster or using four images per cluster from different orientations. The best results (R2 between 69% and 95% in berry detection and between 65% and 97% in cluster weight estimation) were achieved using four images and the Canny algorithm. The model's capability based on image analysis to predict berry weight was 84%. CONCLUSION: The new and low-cost methodology presented here enabled the assessment of cluster yield components, saving time and providing inexpensive information in comparison with current manual methods. © 2014 Society of Chemical Industry.