Implementation of Naïve Bayes Gaussian Algorithm for Real-Time Classification of Broiler Cage Conditions

Abstract

Monitoring large-scale broiler farms poses considerable challenges due to the variable nature of environmental conditions, which have a direct impact on poultry health and productivity. This study proposes a real-time classification system for broiler house conditions, utilizing the Naïve Bayes Gaussian algorithm in conjunction with the Internet of Things (IoT) technology. The system has been developed to address the limitations of manual monitoring by automating the collection of temperature, humidity, and ammonia data through BME-680 and MICS-5524 sensors, which are strategically positioned 30 cm from the floor to optimize ammonia detection. Utilizing a dataset comprising 865 records, meticulously divided into 75% for training (648 records) and 25% for testing (217 records), the model attained an accuracy of 82.03%, a precision of 75.67%, a recall of 82.67%, and an F1-score of 77.67%. A comparative analysis was conducted, which demonstrated significant advantages over alternative classification methods, with Decision Trees achieving 79.5% accuracy and Support Vector Machines reaching 80.8%. The innovation lies in the integration of automated condition classification into an IoT system, enabling rapid responses to environmental changes with processing times of approximately 500 milliseconds from sensing to classification. The system demonstrated an accuracy of 178 data points, with a misclassification rate of 39 out of 217 test samples. The strategic placement of sensors at a height of 30 cm optimizes ammonia detection while ensuring accurate temperature and humidity readings. The system provides historical data, enabling farms to analyze long-term environmental trends, and thereby support data-driven decision-making strategies to enhance broiler welfare and operational efficiency. Usability testing with five poultry farm operators confirmed the dashboard's intuitive design, though recommendations for visual alerts for critical ammonia levels were suggested for future iterations.