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Related Experiment Video

Updated: Jan 20, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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A Non-Invasive Method Based on Computer Vision for Grapevine Cluster Compactness Assessment Using a Mobile Sensing

Fernando Palacios1,2, Maria P Diago1,2, Javier Tardaguila3,4

  • 1Televitis Research Group, University of La Rioja, 26006 Logroño (La Rioja), Spain.

Sensors (Basel, Switzerland)
|September 5, 2019
PubMed
Summary

A new computer vision method accurately estimates grapevine cluster compactness using RGB images. This non-invasive technology offers a practical solution for the wine industry, improving grape quality assessment.

Keywords:
RGBcluster morphologyimage analysismachine learningnon-invasive sensing technologiesprecision viticultureproximal sensing

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Viticulture

Background:

  • Grapevine cluster compactness significantly impacts grape composition, disease susceptibility, and wine quality.
  • Traditional cluster compactness assessment relies on subjective visual inspection, limiting practical application in the wine industry.
  • Objective and efficient methods are needed for real-time cluster compactness evaluation in commercial vineyards.

Purpose of the Study:

  • To develop and validate a novel, non-invasive method for assessing grapevine cluster compactness.
  • To integrate computer vision and machine learning for automated compactness estimation using field-acquired RGB images.
  • To provide a tool for the wine industry enabling efficient and objective cluster compactness assessment.

Main Methods:

  • A mobile sensing platform captured on-the-go red, green, blue (RGB) images of grapevine clusters under artificial illumination.
  • A semi-supervised image segmentation algorithm was employed for initial image processing, followed by automated cluster detection.
  • Gaussian process regression modeled cluster compactness, with models calibrated and validated using expert ratings (OIV 204 standard).

Main Results:

  • The developed computer vision method achieved a coefficient of determination (R²) of 0.68 and a root mean squared error (RMSE) of 0.96 on the test set.
  • Leave-one-out cross-validation (LOOCV) demonstrated robust performance with R² of 0.70 and RMSE of 1.11.
  • The image-based estimations showed strong correlation with average expert ratings for cluster compactness.

Conclusions:

  • The developed computer vision and machine learning approach provides a reliable and non-invasive method for grapevine cluster compactness assessment.
  • This technology is suitable for commercial application in the wine industry, utilizing on-the-go RGB image acquisition.
  • The method offers potential for improved grape quality management and wine production efficiency.