Pollen detection through integrated microfluidics and smartphone-driven deep learning systems

The presence of atmospheric particulates, including bioaerosols such as pollen, significantly impacts air quality and poses potential health risks. Therefore, accurate detection and categorization of these particulates are crucial for evaluating their health implications. This study introduces an innovative, automated system for analyzing atmospheric particulates, combining microfluidic technology with a smartphone-based photonic detection platform. The system integrates a miniaturized optical microscopy module and a consumer-grade high-frame-rate camera, creating an affordable, accessible solution priced at only 50 USD. Such cost-effectiveness promotes the widespread adoption of the technology and facilitates community-driven environmental monitoring. The study developed a compact, rapid, real-time, and continuous air micro-particle sampling and detection platform. The integration of an automatic collection device with a microfluidic detection chip enables continuous sampling and detection. A lightweight object detection network, designated as “PollenDet,” was trained, achieving a mean average precision (mAP) of 94.6%. This technology provides essential insights into the dynamics of airborne particulates, thereby enhancing our understanding of their overall impact.