A Low-Cost Intelligent Water Quality Monitoring System with On-Device Machine Learning and Cloud Integration
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Access to safe drinking water remains a global challenge, worsened by pollution and delays in traditional lab analysis. This paper presents a low-cost, real-time Smart Water Quality Monitoring System using the ESP32 microcontroller. The system measures four crucial parameters - pH, TDS, temperature, and turbidity and transmits data to a cloud backend for remote visualization on a dynamic web dashboard. A key innovation is the integration of an on-device machine learning model (TinyML) for intelligent, real-time categorization of water impurity events. A neural network, trained on a custom generated dataset with TensorFlow and deployed on-device using the TensorFlow Lite for Microcontrollers framework, distinguishes between normal, runoff, and chemical impurity profiles directly on the microcontroller. This approach enables independent anomaly discovery without reliance on cloud connectivity for decision making. The system also features dual-mode SD card recording and an ESP8266-based relay for automated pump control, offering a complete, reproducible, and intelligent result to real-world water monitoring challenges.
