SmartENose: Environmental Health Monitoring System for Cattle Sheds Using Fuzzy System Method
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Abstract
The air quality in livestock barns significantly impacts the health and productivity of animals. This study designs an air quality monitoring system based on the Internet of Things (IoT) utilizing the ESP32 microcontroller and three gas sensors: MQ-135 (ammonia), MQ-4 (methane), and MQ-7 (carbon monoxide). The collected data is processed using the Fuzzy Sugeno logic method, which involves fuzzification, rule base, and defuzzification stages to classify the air conditions into Safe, Alert, or Dangerous categories. The classification results are displayed in real-time through an I2C LCD, the Blynk application, and the SmartENose website developed with PHP and MySQL. Additionally, the system is equipped with LED indicators and a buzzer for early warning notifications. Testing results indicate that the system can detect gas concentrations responsively and accurately, providing air status classifications that align with the actual conditions in the cattle barn. This research demonstrates that the application of IoT technology, supported by Fuzzy Sugeno logic, can be effectively utilized for monitoring and early warning of air quality in agricultural environments.
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