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Chick embryo development assessment and fertility detection using pixel-wise hyperspectral image analysis and deep

Mahdi Ghaderi1, Seyed Ahmad Mireei1, Aminollah Masoumi1

  • 1Department of Biosystems Engineering, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran.

Poultry Science
|November 8, 2025
PubMed
Summary

Hyperspectral imaging (HSI) combined with deep learning accurately detects fertility and embryonic development in poultry eggs. This non-destructive method enhances hatchability efficiency in poultry production.

Keywords:
Deep learning classificationEmbryo developmentFertility detectionPoultry hatchery automationSpectral-spatial modeling

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Area of Science:

  • Agricultural Science
  • Biotechnology
  • Imaging Technology

Background:

  • Efficient poultry production relies on accurate early fertility and embryonic development assessment.
  • Traditional methods for evaluating egg fertility and development are often destructive or labor-intensive.

Purpose of the Study:

  • To develop and validate a non-destructive hyperspectral imaging (HSI) system for real-time fertility and embryonic development monitoring in poultry eggs.
  • To compare the performance of various classification algorithms for HSI data analysis.

Main Methods:

  • Acquisition of full-transmittance hyperspectral images from white-shelled eggs on the first four incubation days.
  • Classification using average spectra with methods including SIMCA, LDA, QDA, and ANN.
  • Pixel-wise classification using ANN, Random Forest, DNN, and CNN for enhanced accuracy and spatial visualization.

Main Results:

  • Artificial Neural Networks (ANN) showed high performance in embryonic stage detection (F1-score: 92.33%).
  • Deep Neural Networks (DNN) achieved the highest stage discrimination (F1-score: 94.95%) in pixel-wise classification.
  • HSI combined with deep learning models demonstrated excellent accuracy for fertility detection across incubation days (up to 100% F1-score).

Conclusions:

  • Hyperspectral imaging offers a promising non-destructive approach for early fertility assessment in poultry.
  • Pixel-wise deep learning classification significantly enhances the accuracy and spatial resolution of fertility and embryonic development monitoring.
  • This integrated HSI and deep learning methodology can improve hatchability efficiency in commercial poultry hatcheries.