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Farooq Jaleel,
Harikrishnan J,
S Gowrilakshmi,
Joe Jose,
Ranjitha Rajan,
Praseeda B Nair,
- Student, Department of Electronics and Communication Engineering, Amal Jyothi College of Engineering, Kerala, India
- Student, Department of Electronics and Communication Engineering, Amal Jyothi College of Engineering, Kerala, India
- Student, Department of Electronics and Communication Engineering, Amal Jyothi College of Engineering, Kerala, India
- Student, Department of Electronics and Communication Engineering, Amal Jyothi College of Engineering, Kerala, India
- Associate Professor, Department of Electronics and Communication Engineering, Amal Jyothi College of Engineering, Kerala, India
- Assitant Professor, Department of Electronics and Communication Engineering, Amal Jyothi College of Engineering, Kerala, India
Abstract
The increasing occurrence of waterborne health risks and the presence of harmful microorganisms in freshwater environments highlight the need for rapid and continuous water quality monitoring. Traditional laboratory-based testing methods often require significant time and manual sampling, limiting their ability to provide immediate information about changing environmental conditions. This work presents AquaSential, a low-cost, portable, and UAV-ready water quality monitoring system designed for real-time assessment of freshwater bodies. The proposed system uses an ESP32 microcontroller integrated with temperature, pH, turbidity, and Total Dissolved Solids (TDS) sensors to continuously acquire important physicochemical water quality parameters. The collected sensor data is processed by the ESP32 and transmitted wirelessly through its built-in Wi-Fi capability to a ground station for real-time visualization and analysis. A basic threshold- based risk classification model is used to identify water conditions that may be favorable for potential biological hazards, including harmful freshwater pathogens such as Naegleria fowleri. The sensing unit is powered by a rechargeable Li-Po battery with an integrated charging and protection circuit and is housed in a lightweight, waterproof enclosure suitable for outdoor deployment. The compact and modular architecture enables future integration with autonomous UAV platforms for monitoring remote, inaccessible, and potentially hazardous water bodies. AquaSential provides a cost- effective foundation for continuous environmental surveillance, early risk identification, and future intelligent water quality monitoring applications.
Keywords: Aquasential, UAV, ESP32 microcontroller, temperature, pH, turbidity, Total Dissolved Solids
References
- E. Ashley Moseman, “Battling brain-eating amoeba: Enigmas surrounding immunity to Naegleria fowleri,” PLoS Pathogens, vol. 16, no. 4, 2020.
- H. T. Lally, I. O’Connor, O. P. Jensen, and C. T. Graham, “Can drones be used to conduct water sampling in aquatic environments? A review,” Science of the Total Environment, vol. 670, pp. 569-575, 2019.
- Irish Mae G. Lariosa et al., “Drone-Based Automatic Water Sampling System,” IEEE Access, vol. 12, pp. 35109-35124, 2024.
- Junliang Wu et al., “Development of a UAV-assisted autonomous water sampling device,” Discover Applied Sciences, 2025.
- C. Koparan, A. B. Koc, C. V. Privette, and C. B. Sawyer, “Autonomous In Situ Measurements of Noncontaminant Water Quality Indicators and Sample Collection with a UAV,” MDPI Water, vol. 11, 2019.
- JAMADAR AS, KAROSHI AA, NILAJI OA, HORATTI RM, DIGRAJ VB. IoT Based Water Quality Monitoring System using ESP32. AIJFR-Advanced International Journal for Research. 2025 Nov 18;6(6).
- Stahl LM, Olson JB. Environmental abiotic and biotic factors affecting the distribution and abundance of Naegleria fowleri. FEMS Microbiology Ecology. 2021 Jan;97(1):fiaa238.
- Dai Pham D, Uong HH. Development of Unmanned Aerial Vehicle and IoT System for Water Quality Monitoring and Water Sampling. Smart Systems and Devices. 2026 May 15;36(2):082-92.
- Durgun Y. Real-time water quality monitoring using AI-enabled sensors: Detection of contaminants and UV disinfection analysis in smart urban water systems. Journal of King Saud University-Science. 2024 Oct 1;36(9):103409.
- Shikany JI, Banks MM, Barnhart EP, Kinsey S, Wright P, Kageyama SA, Merkes CM, Kulesza N, Wylie J, Halonen S, Ortega-Villa AM. Detection of Naegleria fowleri in Thermally Impacted Recreational Waters of Western United States National Parks. ACS Es&t Water. 2026 Mar 2;6(3):1704-15.
Recent Trends in Sensor Research & Technology
| Volume | 13 | |
| 02 | ||
| Received | 23/07/2026 | |
| Accepted | 27/07/2026 | |
| Published | 01/08/2026 | |
| Publication Time | 9 Days |