An IoT and Machine Learning Approach for Predicting Air Quality Parameters in Poultry Houses

Abstract

Purpose: Exceeding permissible gas concentrations in poultry houses causes stress in birds, affects their respiration, reduces egg production, and increases the risk of disease. Therefore, monitoring gas concentrations in poultry houses is critical for hen health and productivity. This study presents a low-cost Internet of things (IoT) and machine learning (ML) system for monitoring and predicting critical air quality parameters in a laying hen house, to enable proactive environmental management in agricultural settings. This study also compared the environmental differences between closed (mechanically controlled) and open (naturally ventilated) systems and determined the feasibility of predicting gas concentrations (CO<inf>2</inf>, MCG, H<inf>2</inf>S) using measurable temperature (Temp.) and humidity (RH) data. Methods: The methodology involved deploying a multi-sensor system (DHT22, MQ-2, MQ-136, MG-811) in a laying hen house, Ministry of Agriculture, Poultry Research Directorate in Iraq, over 25 days. The collected dataset of 1200 samples was divided into 960 samples (80%) for training and 240 samples (20%) for testing to train and evaluate four ML models: decision tree (DT), gradient boosting (GB), linear regression (LR), and random forest (RF). Results: The results show that the closed system produced the highest rate of 9.04 eggs/day compared to 7.20 eggs/day in the open system. Furthermore, the evaluation of the GB model confirmed its high predictive accuracy, with coefficients of determination (R²) of 0.99 for MCG, 0.88 for CO₂, and 0.83 for H₂S. This strong performance substantiates that (Temp./RH) serves as robust, non-linear predictors for inferring air quality parameters. Conclusion: The proposed integration of IoT and ML provides a low-cost and accurate approach to monitoring gas concentrations in real-time, enabling timely intervention and continuous environmental improvement in poultry housing.