In this paper, we present the design of our prototype of an automated real-time and affordable pollen sensing system. The design consists of three main subsystems: (1) a trap with automatic filtering, (2) a particle concentration system, and (3) a digital microscope with autofocus. The prototype shows effective particle gathering, filtering and concentration in a tiny sized area. As a result, we reduce particle loss and improve image quality taken by the optical system when searching and autofocusing on pollen grains.

Our first prototype collects raw time-stamped data and transmits these to the backend server where we plan to run the detection and classification algorithms to extract accurate pollen counts from microscopic images. The key advantage of processing images at the backend is that we let the experts undertake corrective actions and help the system learn to detect and classify pollen using state-of-the-art interactive imitation learning algorithms. The final model can then run locally on embedded hardware.

N. Cao, O. Saukh, L. Thiele,  An automated real-time and affordable airborne pollen sensing system: poster abstract, Proceedings of the 18th International Conference on Information Processing in Sensor Networks (2019) 321-322