An integrated system for automated measurement of airborne pollen based on electrostatic enrichment and image analysis with machine vision

Abstract

Here we describe an automated and compact pollen detection system that integrates enrichment, in-situ detection and self-cleaning modules. The system can achieve continuous capture and enrichment of pollen grains in air samples by electrostatic adsorption. The captured pollen grains are imaged with a digital camera, and an automated image analysis based on machine vision is performed, which enables a quantification of the number of pollen particles as well as a preliminary classification into two types of pollen grains. In order to optimize and evaluate the system performance, we developed a testing approach that utilizes an airflow containing a precisely metered amount of pollen particles surrounded by a sheath flow to achieve the generation and lossless transmission of standard gas samples. We studied various factors affecting the pollen capture efficiency, including the applied voltage, air flow rate and humidity. Under optimized conditions, the system was successfully used in the measurement of airborne pollen particles within a wide range of concentrations, spanning 3 orders of magnitude.

Keywords: Automated analyzer; Electrostatic enrichment; Machine vision; Pollen measurement.