Volume 2 - Issue 2, March - April 2026

Water contamination caused by pathogenic microorganisms poses a serious risk to public health and environmental safety. Conventional laboratory-based water quality testing methods are often time-consuming, expensive, and unsuitable for continuous monitoring. To address these limitations, this paper presents an AIenabled embedded system designed for the automated detection of waterborne microorganisms. The proposed system integrates embedded hardware with intelligent data processing techniques to analyze sensor inputs and identify microbial contamination in real time. Machine learning algorithms are employed to improve detection accuracy by learning patterns associated with different microorganisms. The embedded architecture enables low-power operation, portability, and rapid response, making the system suitable for on-site water quality assessment. The proposed approach enhances the efficiency, reliability, and scalability of microorganism detection, offering a practical solution for smart water monitoring applications in environmental and public health domains.

Bharath V, Dr.Sreejith Vignesh B P,"AI-Enabled Embedded System for Automated Detection of Waterborne Microorganisms" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(2): Page(695-703) Mar-Apr 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.