Artificial intelligence (AI) based rapid water testing system

This research introduces a next-generation AI-powered rapid water testing system designed for real-time, high-precision water quality monitoring in both industrial and domestic environments. The system is built around an ESP32-C6 microcontroller and features a 1.47-inch TFT display for immediate on-site data visualization. It integrates five complementary analytical techniques: capacitance measurement, resistance analysis, ultraviolet (UV) exposure, infrared (IR) absorption, and Raman spectroscopy to assess water quality within seconds. The multi-modal sensing framework enables detection of a wide range of contaminants. Capacitance and resistance measurements identify inorganic ions, dissolved salts, and microbial activity, while UV and IR absorption provide rapid insights into organic pollutants. Raman spectroscopy delivers molecular-level fingerprinting, allowing advanced contaminant identification. Together, these techniques enable estimation of key water quality indicators such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), total coliforms, and fecal coliforms, as well as real-time detection of heavy metals, synthetic dyes, microplastics, and pathogens including E. coli. An embedded AI model, trained on a diverse dataset of water samples, analyzes sensor outputs to recognize complex contamination patterns with high reliability. The result is a portable, energy-efficient, and cost-effective solution capable of delivering immediate water quality assessments and supporting informed decision-making for water safety. Despite its advantages, the system has limitations. Certain parameters, such as specific pesticide residues, cannot be measured with high accuracy due to sensor selectivity and hardware constraints. Additionally, calibration or modular expansion may be required for highly specialized or extreme conditions.