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Image analysis-based modelling for flower number estimation in grapevine.

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Summary

Accurately estimating grapevine flower numbers per inflorescence using image analysis and a nonlinear model provides a reliable tool for yield prediction across varieties. This non-invasive method aids in assessing harvest yield efficiently.

Keywords:
computer visionfloweringfruit set ratemulti-variety linear modelsnon-linear modelsyield prediction

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Area of Science:

  • Agricultural Science
  • Horticulture
  • Plant Science

Background:

  • Grapevine flower number per inflorescence is crucial for yield assessment.
  • Previous research focused on technological tools for image analysis and predictive modeling.
  • The performance of variety-independent models for yield prediction across diverse grapevine varieties remains unevaluated.

Purpose of the Study:

  • To evaluate variety-independent predictive models for grapevine yield estimation.
  • To assess the yield prediction capabilities of an image analysis tool across multiple grapevine varieties.
  • To develop a fast, non-invasive, and reliable method for estimating harvest yield.

Main Methods:

  • Acquisition of inflorescence images from 11 Vitis vinifera L. varieties under field conditions.
  • Manual and automated calculation of flower number per inflorescence using image analysis algorithms.
  • Calibration and evaluation of linear (single-variable, multivariable) and nonlinear variety-independent models.

Main Results:

  • An integrated tool combining image analysis and a nonlinear model demonstrated high performance and robustness (RPD = 8.32, RMSE = 37.1).
  • Yield estimation using flower number showed strong correlation with fruit set rate (R² = 0.79) and average berry weight (R² = 0.91).
  • The developed image analysis algorithm and nonlinear model accurately estimated flower number per inflorescence.

Conclusions:

  • The study validates an accurate image analysis algorithm and nonlinear model for estimating grapevine flower number, applicable across different varieties.
  • This approach offers a fast, non-invasive, and reliable tool for predicting yield at harvest.
  • The findings support the use of this technology for efficient agricultural management and yield forecasting.