Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters

  1. Tello, J. 3
  2. Cubero, S. 13
  3. Blasco, J. 1
  4. Tardaguila, J. 3
  5. Aleixos, N. 2
  6. Ibáñez, J. 3
  1. 1 Instituto Valenciano de Investigaciones Agrarias
    info

    Instituto Valenciano de Investigaciones Agrarias

    Moncada i Reixac, España

    ROR https://ror.org/00kx3fw88

  2. 2 Universidad Politécnica de Valencia
    info

    Universidad Politécnica de Valencia

    Valencia, España

    ROR https://ror.org/01460j859

  3. 3 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

Revista:
Journal of the Science of Food and Agriculture

ISSN: 0022-5142

Año de publicación: 2016

Volumen: 96

Número: 13

Páginas: 4575-4583

Tipo: Artículo

beta Ver similares en nube de resultados
DOI: 10.1002/JSFA.7675 PMID: 26910811 SCOPUS: 2-s2.0-84982113314 WoS: WOS:000382838100035 GOOGLE SCHOLAR

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

Repositorio institucional: lockAcceso abierto Editor

Resumen

BACKGROUND: Grapevine cluster morphology influences the quality and commercial value of wine and table grapes. It is routinely evaluated by subjective and inaccurate methods that do not meet the requirements set by the food industry. Novel two-dimensional (2D) and three-dimensional (3D) machine vision technologies emerge as promising tools for its automatic and fast evaluation. RESULTS: The automatic evaluation of cluster length, width and elongation was successfully achieved by the analysis of 2D images, significant and strong correlations with the manual methods being found (r = 0.959, 0.861 and 0.852, respectively). The classification of clusters according to their shape can be achieved by evaluating their conicity in different sections of the cluster. The geometric reconstruction of the morphological volume of the cluster from 2D features worked better than the direct 3D laser scanning system, showing a high correlation (r = 0.956) with the manual approach (water displacement method). In addition, we constructed and validated a simple linear regression model for cluster compactness estimation. It showed a high predictive capacity for both the training and validation subsets of clusters (R2 = 84.5 and 71.1%, respectively). CONCLUSION: The methodologies proposed in this work provide continuous and accurate data for the fast and objective characterisation of cluster morphology. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry