A Precision Method for Monitoring Poultry Floor Distribution


In commercial poultry houses, chicken density and distribution in drinking, feeding, and resting zones are critical factors for evaluating flock production, bird health/well-being, and welfare. Proper distribution of chickens in the house could be an indication of animal wellbeing and house environmental management such as ventilation and litter quality concerns.


Currently, daily routine inspection of broiler flock distribution in commercial grow-out houses is done manually, which is labor intensive and time consuming. This task requires an attentive observer to monitor bird distribution and behavior and thereby gathering information that can help identify early indications of bird health and welfare concerns.


Dr. Lilong Chai (a poultry engineering specialist in UGA poultry science department) is studying artificial intelligence-based technology (e.g., machine vision-based method) for monitoring floor distribution of chickens automatically. An early-version of monitoring system including the data collection and model/algorithms for image analysis has been developed and tested in research poultry houses at UGA.


The key technology of this system is a deep learning model that Dr. Chai’s team developed. This system has been tested with the accuracy of 94% or higher in monitoring birds’ distribution in feeding and drinking zones. The current finding provides the basis for developing an automated approach to monitoring poultry floor distribution and behaviors in commercial production systems. Eventually, this research will benefit U.S’s $40+B (sales) poultry industry by enhancing flock monitoring, well-being and risk management.

State Issue

Animal Production


  • Year: 2020
  • Geographic Scope: National
  • County: Clarke
  • Location: College Station, Athens
  • Program Areas:
    • Agriculture & Natural Resources


    Chai, Lilong


CAES Collaborator(s)

  • Aggrey, Samuel E.

Non-CAES Collaborator(s)

  • Oladeinde, Ade (USDA ARS)
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