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A Computer Vision-Based Automatic System for Egg Grading and Defect Detection.

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  • 1Department of Poultry Science, University of Georgia, Athens, GA 30602, USA.

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|July 29, 2023
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This study introduces a novel AI model combining deep learning and machine vision for automated egg classification and weight prediction. The system accurately identifies defects and sorts eggs, improving poultry production efficiency and product quality.

Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Defective eggs reduce profitability in the poultry industry, especially in cage-free systems.
  • Automated grading and weight-sorting systems exist, but few integrate deep learning for combined classification and weighing.
  • Floor eggs present a significant challenge in cage-free laying hen production.

Purpose of the Study:

  • To develop and evaluate a two-stage deep learning model for joint egg classification and weight prediction.
  • To address the gap in integrated machine vision and deep learning for egg quality assessment.
  • To create an automated system for detecting defects and measuring egg weight simultaneously.

Main Methods:

  • A two-stage model utilizing real-time multitask detection (RTMDet) and Random Forest algorithms was developed.
Keywords:
deep learningdefect detectionegg qualityegg weightlaying hen production

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  • Convolutional Neural Networks (CNNs) and regression techniques were employed for joint classification and weighing.
  • RTMDet extracted egg features (major/minor axes) for classification, while Random Forest predicted weight.

    • Main Results:

    • The model achieved a classification accuracy of 94.8% and an R-squared (R2) value of 96.0% for weight prediction.
    • The system successfully excluded non-standard-sized eggs and those with exterior defects like cracks, stains, or calcium deposits.
    • The detector classifies eggs into five categories (intact, crack, bloody, floor, non-standard) and measures weight up to jumbo size.

    Conclusions:

    • The integrated deep learning and machine vision model offers a novel solution for automated egg sorting and weighing.
    • Implementation can reduce industry costs, increase productivity, and enhance consumer product quality.
    • This system represents a significant advancement in automated poultry product assessment.