Assessment of flower number per inflorescence in grapevine by image analysis under field conditions

  1. Diago, M.P. 1
  2. Sanz-Garcia, A. 1
  3. Millan, B. 1
  4. Blasco, J. 2
  5. Tardaguila, J. 1
  1. 1 Instituto de Ciencias de la Vid y del Vino
    info

    Instituto de Ciencias de la Vid y del Vino

    Logroño, España

    GRID grid.481584.4

  2. 2 Instituto Valenciano de Investigaciones Agrarias
    info

    Instituto Valenciano de Investigaciones Agrarias

    Moncada i Reixac, España

    GRID grid.419276.f

Journal:
Journal of the Science of Food and Agriculture

ISSN: 0022-5142

Year of publication: 2014

Volume: 94

Issue: 10

Pages: 1981-1987

Type: Article

Export: RIS
DOI: 10.1002/jsfa.6512 SCOPUS: 2-s2.0-84901924023 WoS: 000337612400015 GOOGLE SCHOLAR

Metrics

Cited by

  • Scopus Cited by: 29 (14-07-2021)

Journal Citation Reports

  • Year 2014
  • Journal Impact Factor: 1.714
  • Best Quartile: Q1
  • Area: AGRICULTURE, MULTIDISCIPLINARY Quartile: Q1 Rank in area: 7/56 (Ranking edition: SCIE)
  • Area: CHEMISTRY, APPLIED Quartile: Q2 Rank in area: 27/72 (Ranking edition: SCIE)
  • Area: FOOD SCIENCE & TECHNOLOGY Quartile: Q2 Rank in area: 46/123 (Ranking edition: SCIE)

SCImago Journal Rank

  • Year 2014
  • SJR Journal Impact: 0.814
  • Best Quartile: Q1
  • Area: Agronomy and Crop Science Quartile: Q1 Rank in area: 56/346
  • Area: Food Science Quartile: Q1 Rank in area: 49/308
  • Area: Biotechnology Quartile: Q2 Rank in area: 81/321
  • Area: Nutrition and Dietetics Quartile: Q2 Rank in area: 49/122

CiteScore

  • Year 2014
  • CiteScore of the Journal : 3.6
  • Area: Agronomy and Crop Science Percentile: 85
  • Area: Food Science Percentile: 83
  • Area: Biotechnology Percentile: 68
  • Area: Medicine (all) Percentile: 61
  • Area: Nutrition and Dietetics Percentile: 60

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Abstract

BACKGROUND: Flowers, flowering and fruit set are key determinants of grapevine yield. Currently, practical methods to assess the flower number per inflorescence, necessary for fruit set estimation, are time and labour demanding. This work aims at developing a simple, cheap, fast, accurate and robust machine vision methodology to be applied to RGB images taken under field conditions, to estimate the number of flowers per inflorescence automatically. RESULTS: Ninety images of individual inflorescences of Vitis vinifera L. cultivars Tempranillo, Graciano and Carignan were acquired in the vineyard with a pocket RGB camera prior to flowering, and used to develop and test the 'flower counting' algorithm. Strong and significant relationships, with R2 above 80% for the three cultivars were observed between actual and automated estimation of inflorescence flower numbers, with a precision exceeding 90% for all cultivars. CONCLUSION: The developed algorithm proved that the analysis of digital images captured by pocket cameras under uncontrolled outdoors conditions was able to automatically provide a useful estimation of the number of flowers per inflorescence of grapevines at early stages of flowering. © 2013 Society of Chemical Industry.