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An identification method for perforated embryo eggs' cracks using fractal matrices.

Mingyan Zhao1, Ying Liu1

  • 1State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, China.

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|December 24, 2025
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Summary

This study introduces a new fractal-based method to automatically detect cracked embryo eggs for vaccine production. The approach significantly improves accuracy and efficiency in identifying defective eggs, reducing manual labor and misjudgments.

Keywords:
Crack detectionFractal matrixLogistic regressionPerforated embryo eggs

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Area of Science:

  • Biotechnology
  • Agricultural Science
  • Image Analysis

Background:

  • Manual identification of cracked embryo eggs is inefficient and error-prone.
  • Automated methods are needed to improve vaccine production quality control.

Purpose of the Study:

  • To develop an automated method for identifying cracked embryo eggs using fractal analysis.
  • To improve the accuracy and efficiency of defective egg removal in vaccine production.

Main Methods:

  • Employed box-counting fractal method for image analysis.
  • Constructed a fractal matrix in the perforation region to extract damage features.
  • Combined fractal dimension with damage features to create a crack feature vector.
  • Utilized a logistic regression model for crack classification.

Main Results:

  • Achieved 98.9% accuracy in differentiating qualified eggs from cracked ones.
  • Successfully distinguished between different crack types (linear, star-shaped, net-shaped).
  • Developed a reliable automated system for defective egg removal.

Conclusions:

  • The fractal matrix method effectively digitizes eggshell crack information.
  • Precise discriminant criteria were established for automated crack detection.
  • This method provides a reliable basis for automated removal of defective cracked embryo eggs, enhancing vaccine production.