Applications of computer vision techniques in viticulture to assess canopy features, cluster morphology and berry size

  1. Tardaguila, J. 24
  2. Diago, M.P. 2
  3. Millan, B. 2
  4. Blasco, J. 1
  5. Cubero, S. 1
  6. Aleixos, N. 3
  1. 1 Instituto Valenciano de Investigaciones Agrarias

    Instituto Valenciano de Investigaciones Agrarias

    Moncada i Reixac, España

    GRID grid.419276.f

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

    Instituto de Ciencias de la Vid y del Vino

    Logroño, España

    GRID grid.481584.4

  3. 3 Universidad Politécnica de Valencia

    Universidad Politécnica de Valencia

    Valencia, España

    GRID grid.157927.f

  4. 4 Universidad de La Rioja

    Universidad de La Rioja

    Logroño, España

    GRID grid.119021.a

Acta Horticulturae

ISSN: 0567-7572

Year of publication: 2013

Volume: 978

Pages: 77-84

Type: Article

Export: RIS


Cited by

  • Scopus Cited by: 1 (14-07-2021)

SCImago Journal Rank

  • Year 2013
  • SJR Journal Impact: 0.195
  • Best Quartile: Q4
  • Area: Horticulture Quartile: Q4 Rank in area: 58/76


  • Year 2013
  • CiteScore of the Journal : 0.3
  • Area: Horticulture Percentile: 9

Related Projects


Computer vision systems are powerful tools to automate inspection tasks in agriculture. Typical target applications of such systems include grading, quality estimation, yield prediction and monitoring, among others. The capabilities of an artificial vision system go beyond the limited human capacity to evaluate long-term processes objectively and provide valuable data to take decisions that will have great influence in later operations. This work explores the application of machine vision techniques in viticulture from several approaches. The first approach is aimed at working outdoors, developing in-field systems capable of assessing the canopy features of the vineyard (Vitis vinifera L.) by taking digital images and applying computer vision systems. The second approach is aimed at analysing cluster morphology using image analysis. Berry number per cluster and cluster weight were estimated using several algorithms of image processing. Lately, machine vision has been used as a tool to automate the measurement of berry size and weight under laboratory conditions. Manual measurement of the canopy features and yield components are tedious and subjective tasks that can be time-consuming and labour demanding. In this regard, by means of computer vision techniques, a large set of samples can be automatically measured, saving time and providing more objective and precise information.