Web-Based Environmental Monitoring System Design and Dust Concentration Modeling for Dairy Barns

This study focuses on designing and evaluating a web-based environmental monitoring system for dairy barns, targeting dust concentration and temperature/humidity levels. A prototype device integrating Arduino technology with GSM module was developed to enable real-time data transmission to a web-based platform. The system was tested in a small-scale dairy barn, where it effectively monitored dust, temperature, and humidity while calculating the Temperature Humidity Index (THI). Statistical analysis using multiple regression (MR) and artificial neural networks (ANN) revealed that temperature and humidity significantly influence dust concentrations, with both models achieving strong predictive performance (MR: R² = 0.7, ANN: R² = 0.71). These results indicate that both methods can accurately predict dust levels based on environmental conditions. This low-cost solution not only improves air quality monitoring but also offers a viable tool for controlling heat stress in dairy barns, enhancing animal welfare and productivity. The system's affordability makes it accessible for small family farms and adaptable for broader agricultural applications.