Innovative System for Monitoring Poultry Air Quality

Strategic Insights -

Prairie View A&M University researchers have published new data on a cutting-edge system that uses cloud computing, Artificial Intelligence, and Internet of Things technologies to measure real-time gas concentrations in poultry houses. The system aims to maintain birds' health and improve the economic viability of the poultry industry. The researchers found that traditional air quality monitoring systems are often labor-intensive and cannot detect sudden environmental changes. The new system uses a machine learning model to predict gas emission levels and triggers automatic alerts when gas levels exceed safe thresholds.

Key Takeaways:

  • The system combines cloud computing, AI, and IoT technologies to measure real-time gas concentrations in poultry houses.
  • The system uses a machine learning model to predict gas emission levels and triggers automatic alerts when gas levels exceed safe thresholds.
  • Traditional air quality monitoring systems are often labor-intensive and cannot detect sudden environmental changes.
  • Elevated concentrations of Carbon Dioxide (CO2), Methane (CH4), and Ammonia (NH3) have draconian effects on bird health, feed efficiency, growth rate, reproduction efficiency, and mortality rate.
  • The system shows a significant diurnal trend in CO2 concentrations, peaking in the afternoon and reaching its lowest levels in the morning.
  • The system authors include Sejal Bhattad, Ahmed Abdelmoamen Ahmed, Ahmed A. A. Abdel-Wareth, and Jayant Lohakare.
  • The research was funded by the National Science Foundation and the USDA-NIFA-Evans Allen.

Statistics:

  • CO2 concentrations peaked at approximately 570 ppm during the afternoon.
  • Traditional air quality monitoring systems are often operated manually, labor-intensive, and cannot detect sudden environmental changes.
  • The system uses real-time sensor feeds to measure gas concentrations and transmits data to a cloud-based platform.
  • The platform stores, displays, and processes the data, allowing for automatic alerts when gas levels exceed safe thresholds.
  • The machine learning model was trained using historical sensory data to predict the next-day gas emission levels.

Sources:

  • NewsRx. Prairie View A&M University Researchers Have Published New Data on Agriculture (An IoT-Based System for Measuring Diurnal Gas Emissions of Laying Hens in Smart Poultry Farms). Agriculture Week. September 11, 2025; p 226.
  • An IoT-Based System for Measuring Diurnal Gas Emissions of Laying Hens in Smart Poultry Farms. AgriEngineering, 2025,7(8):267.

(https://doi.org/10.3390/agriengineering7080267)