Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{195848,
        author = {P. Yamini Sri and M. Pavani and N. Pravallika},
        title = {FPGA-Based Real-Time Water Quality Monitoring and Classification System Using Multi-Sensor Data Processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {874-881},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195848},
        abstract = {Access to clean and safe water is essential for public health, agriculture, and industrial applications. Conventional water quality monitoring systems based on microcontrollers suffer from limitations such as low processing speed, sequential operation, and reduced accuracy. To address these challenges, this paper presents an FPGA-based real-time water quality monitoring and classification system using multi-sensor data processing.
The proposed system integrates multiple sensors, including pH, turbidity, temperature, and Total Dissolved Solids (TDS), to continuously monitor water quality parameters. Sensor data is acquired through an external Analog- to-Digital Converter (ADC) and processed in parallel using FPGA architecture, enabling high-speed and efficient computation. The system classifies water quality into different categories based on predefined threshold values aligned with standard guidelines.
Furthermore, the processed data is transmitted using an ESP32 Wi-Fi module to the ThingSpeak cloud platform, enabling real-time remote monitoring and data visualization. A web-based interface and LCD display pro- vide both remote and local access to water quality information.
Experimental results demonstrate improved performance in terms of processing speed, accuracy, and real-time responsiveness compared to conventional systems. The proposed system offers a scalable, cost-effective, and reliable solution for modern water quality monitoring applications.},
        keywords = {FPGA, Water Quality Monitoring, Real-Time Systems, ESP32, ThingSpeak, Multi-Sensor Data Processing, IoT, Environmental Monitoring},
        month = {April},
        }