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Related Experiment Video

Updated: Mar 19, 2026

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System
05:10

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

Published on: March 17, 2023

3.8K

A deep-learning-based early warning system for abnormal eye conditions in chickens.

Yu-Chieh Chen1, Jen-Hung Huang2, Hsiu-Yun Hu2

  • 1Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Taichung, Taiwan; Institute of Applied Mechanics, National Taiwan University, Taipei, Taiwan.

Poultry Science
|March 18, 2026
PubMed
Summary

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A new deep learning system monitors chicken eye health to predict flock decline. This AI tool offers early warnings for disease, reducing labor and economic losses in poultry farming.

Area of Science:

  • Animal Science
  • Artificial Intelligence
  • Veterinary Medicine

Background:

  • Traditional poultry health monitoring is labor-intensive and risks disease spread.
  • Current methods require frequent manual inspection of large chicken flocks.
  • Automated health assessment is needed to improve efficiency and biosecurity.

Purpose of the Study:

  • To develop a deep learning-based early warning system for detecting abnormal chicken eye conditions.
  • To automate the monitoring of flock health in commercial poultry farms.
  • To assess the potential of abnormal eye conditions as an indicator of impending flock health issues.

Main Methods:

  • Utilized a pan-tilt-zoom camera for image acquisition within poultry houses.
  • Annotated images into 'normal' and 'abnormal' categories, applying data augmentation.
Keywords:
Chicken eyeDeep learningDigital health managementPan–tilt–zoom camera

Related Experiment Videos

Last Updated: Mar 19, 2026

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System
05:10

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

Published on: March 17, 2023

3.8K
  • Trained a You Only Look Once v7 deep learning model for abnormal eye detection.
  • Deployed the model on a commercial farm to monitor three production cycles.

    • Main Results:

    • The deep learning model achieved high performance metrics (precision: 0.944, recall: 0.831, F1: 0.884).
    • Time-lag analysis revealed abnormal eye conditions preceded mortality by 3-7 days (max correlation ρ = 0.7304 at 5-day lag).
    • An optimal threshold of 4.5% abnormal eyes indicated potential health decline with high sensitivity and consistency.

    Conclusions:

    • Deep learning-based detection of abnormal chicken eyes serves as a reliable early warning indicator for flock health deterioration.
    • The developed system can significantly reduce manual labor and mitigate economic losses in poultry production.
    • This digital health monitoring tool enables timely interventions, enhancing overall flock management and biosecurity.