Dr. ZHANG Yunping, SherrySherryDr. ZHANG YunpingTian, YunfeiYunfeiTianLi, YuxingYuxingLiLam, Edmund Y.Edmund Y.Lam2025-08-272025-08-272025Zhang, Y., Tian, Y., Li, Y., & Lam, E. Y. (25 Jun 2025). A portable AI-powered rotifer-tracking system for in-situ water quality assessment. SPIE Optical Metrology 2025, Munich, Germany.https://spie.org/optical-metrology/presentation/A-portable-AI-powered-rotifer-tracking-system-for-in-situ/13570-27http://hdl.handle.net/20.500.11861/24665Conventional optical microscopes face limitations in detecting certain water pollutants, such as transparent microplastics. Rotifers, microscopic freshwater organisms, are effective bio-indicators for assessing aquatic health due to their sensitivity to environmental changes. However, traditional rotifer-based water quality assessments are hindered by complex sample preparation, inaccuracies from sample transportation, and the use of bulky optical systems that require specialized expertise. To address these challenges, we present a low-cost, portable, AI-powered rotifer tracking microscope for on-site water quality assessment. This system integrates a 3D-printed open-source microscope with custom AI algorithms on a Raspberry Pi, enabling automated rotifer tracking and analysis without the need for sample transportation or specialized skills. Its compact, user-friendly, and cost-efficient design enhances accessibility for resource-limited areas. Validation with samples polluted by microplastics demonstrated the system’s ability to detect changes in the behavioral dynamics of rotifers, proving its reliability for real-time aquatic health monitoring. This innovative system offers a practical, scalable solution for water quality management in diverse settings.enConference Paper