Image analysis-based modelling for flower number estimation in grapevine

  1. Millan, B. 1
  2. Aquino, A. 1
  3. Diago, M.P. 1
  4. 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

    ROR https://ror.org/01rm2sw78

Journal:
Journal of the Science of Food and Agriculture

ISSN: 0022-5142

Year of publication: 2017

Volume: 97

Issue: 3

Pages: 784-792

Type: Article

DOI: 10.1002/JSFA.7797 SCOPUS: 2-s2.0-84973520060 WoS: WOS:000395329000009 GOOGLE SCHOLAR

More publications in: Journal of the Science of Food and Agriculture

Metrics

Cited by

  • Scopus Cited by: 28 (24-02-2024)
  • Web of Science Cited by: 23 (28-10-2023)
  • Dimensions Cited by: 27 (06-01-2024)

JCR (Journal Impact Factor)

  • Year 2017
  • Journal Impact Factor: 2.379
  • Journal Impact Factor without self cites: 2.258
  • Article influence score: 0.542
  • Best Quartile: Q1
  • Area: AGRICULTURE, MULTIDISCIPLINARY Quartile: Q1 Rank in area: 8/57 (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: 42/133 (Ranking edition: SCIE)

SCImago Journal Rank

  • Year 2017
  • SJR Journal Impact: 0.822
  • Best Quartile: Q1
  • Area: Food Science Quartile: Q1 Rank in area: 53/360
  • Area: Agronomy and Crop Science Quartile: Q1 Rank in area: 53/382
  • Area: Biotechnology Quartile: Q2 Rank in area: 77/407
  • Area: Nutrition and Dietetics Quartile: Q2 Rank in area: 51/136

Scopus CiteScore

  • Year 2017
  • CiteScore of the Journal : 4.2
  • Area: Agronomy and Crop Science Percentile: 86
  • Area: Food Science Percentile: 83
  • Area: Biotechnology Percentile: 70
  • Area: Nutrition and Dietetics Percentile: 63

Journal Citation Indicator (JCI)

  • Year 2017
  • Journal Citation Indicator (JCI): 1.07
  • Best Quartile: Q1
  • Area: AGRICULTURE, MULTIDISCIPLINARY Quartile: Q1 Rank in area: 10/71
  • Area: CHEMISTRY, APPLIED Quartile: Q1 Rank in area: 11/73
  • Area: FOOD SCIENCE & TECHNOLOGY Quartile: Q1 Rank in area: 29/157

Dimensions

(Data updated as of 06-01-2024)
  • Total citations: 27
  • Recent citations (2 years): 8
  • Relative Citation Ratio (RCR): 0.18
  • Field Citation Ratio (FCR): 4.98

Abstract

BACKGROUND: Grapevine flower number per inflorescence provides valuable information that can be used for assessing yield. Considerable research has been conducted at developing a technological tool, based on image analysis and predictive modelling. However, the behaviour of variety-independent predictive models and yield prediction capabilities on a wide set of varieties has never been evaluated. RESULTS: Inflorescence images from 11 grapevine Vitis vinifera L. varieties were acquired under field conditions. The flower number per inflorescence and the flower number visible in the images were calculated manually, and automatically using an image analysis algorithm. These datasets were used to calibrate and evaluate the behaviour of two linear (single-variable and multivariable) and a nonlinear variety-independent model. As a result, the integrated tool composed of the image analysis algorithm and the nonlinear approach showed the highest performance and robustness (RPD = 8.32, RMSE = 37.1). The yield estimation capabilities of the flower number in conjunction with fruit set rate (R2=0.79) and average berry weight (R2=0.91) were also tested. CONCLUSION: This study proves the accuracy of flower number per inflorescence estimation using an image analysis algorithm and a nonlinear model that is generally applicable to different grapevine varieties. This provides a fast, non-invasive and reliable tool for estimation of yield at harvest. © 2016 Society of Chemical Industry.