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Assessing exterior egg quality indicators using machine vision.

A V Vasileva1, E V Gorbunova1, A S Vasilev1

  • 1a Department of Optical-Electronic Devices and Systems , ITMO University , Saint Petersburg , Russian Federation.

British Poultry Science
|September 18, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a machine vision system for automated chicken egg sorting, accurately analyzing exterior quality indicators like shape and mottling. The developed algorithm offers a reliable alternative to manual grading, enhancing sorting efficiency.

Keywords:
Computer techniquesegg classificationegg qualityegg shellmachine visionnon-destructive testing

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Food Science

Background:

  • Automated quality assessment in the poultry industry is crucial for efficiency and consistency.
  • Manual grading of chicken eggs is labor-intensive and prone to subjective errors.
  • Objective, automated methods are needed to evaluate exterior egg parameters.

Purpose of the Study:

  • To develop and validate a machine vision algorithm for analyzing exterior chicken egg parameters.
  • To automate the primary sorting stage in egg processing.
  • To provide an objective and consistent method for assessing egg quality.

Main Methods:

  • Development of a machine vision algorithm using predetermined thresholds.
  • Integration of image-based analysis with candling methods.
  • Analysis of 400 chicken egg samples.

Main Results:

  • The algorithm accurately calculated geometric dimensions, shape index, and mottling grade.
  • High correlation (0.93) was found for shape index compared to manual methods.
  • Minimal mean differences in radii (0.095 ± 0.058 mm sharp end, 0.080 ± 0.047 mm blunt end) were observed.

Conclusions:

  • The machine vision method enables automatic assessment of exterior egg quality factors.
  • The system reduces reliance on grader expertise, ensuring consistent quality evaluation.
  • This technology can significantly improve the efficiency and objectivity of egg sorting processes.