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Non-Destructive Detection Model and Device Development for Duck Egg Freshness.

Qian Yan1, Qiaohua Wang1,2,3, Meihu Ma4

  • 1College of Engineering, Huazhong Agricultural University, Wuhan 430070, China.

Foods (Basel, Switzerland)
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel machine vision system for non-destructive duck egg freshness grading. The developed model accurately assesses egg quality, improving safety and industry standards.

Keywords:
Haugh UnitsLAB color space clusteringimage segmentationmulti-angle transmission imagingpoultry egg gradingweighted Otsu algorithm

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Area of Science:

  • Agricultural Engineering
  • Food Science
  • Computer Vision

Background:

  • Traditional duck egg freshness detection methods lack accuracy and adaptability to varying shell colors.
  • Objective grading is crucial for food safety and consumer trust in the poultry industry.

Purpose of the Study:

  • To develop a non-destructive detection model and integrated device for accurate duck egg freshness grading.
  • To improve adaptability to different duck eggshell colors using machine vision and optical analysis.

Main Methods:

  • Designed a four-sided yolk transmission imaging system utilizing grayscale conversion, improved Otsu algorithm, histogram equalization, and K-means clustering for yolk segmentation.
  • Constructed a freshness grading model based on the yolk projected area ratio and Haugh Units (HU), with thresholds optimized using the Youden index.
  • Developed an automated testing device with a roller-rotating mechanism and LED cool white light source for consistent imaging across different shell colors.

Main Results:

  • The developed model achieved 91.3% overall freshness grade discrimination accuracy, with high sensitivity (97.1%) and specificity (98.9%) for inedible eggs (Grade B, HU < 60).
  • The automated device demonstrated 88.5% discrimination accuracy with a rapid detection time of ≤5 seconds per egg.
  • The system provides real-time output of yolk area ratio, predicted HU value, and freshness level.

Conclusions:

  • The machine vision-based system offers a high-precision, low-cost solution for refined duck egg grading.
  • The integrated device effectively addresses the limitations of traditional methods and adapts to diverse eggshell colors.
  • This technology enhances food safety and efficiency in the poultry egg industry.