A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques

The monitoring of airborne pollen has received much attention over the last decade, as the prevalence of pollen-induced allergies is constantly increasing. Today, the most common technique to identify airborne pollen species and to monitor their concentrations is based on manual analysis. Here, we present a new, low-cost, real-time optical pollen sensor, called Beenose, that automatically counts and identifies pollen grains by performing measurements at multiple scattering angles. We describe the data pre-processing steps and discuss the various statistical and machine learning methods that have been implemented to distinguish different pollen species. The analysis is based on a set of 12 pollen species, several of which were selected for their allergic potency. Our results show that Beenose can provide a consistent clustering of the pollen species based on their size properties, and that pollen particles can be separated from non-pollen ones. More importantly, 9 out of 12 pollen species were correctly identified with a prediction score exceeding 78%. Classification errors occur for species with similar optical behaviour, suggesting that other parameters should be considered to provide even more robust pollen identification.

Automated summary

The monitoring of airborne pollen has become increasingly important due to the rise in pollen-induced allergies. This paper introduces a new optical pollen sensor, Beenose, which can automatically count and identify pollen grains in real-time using measurements at multiple scattering angles. Different statistical and machine learning methods are discussed for distinguishing different pollen species. The results show that Beenose can accurately cluster pollen species based on their size properties and separate pollen particles from non-pollen ones. However, further parameters need to be considered for more robust pollen identification, particularly for species with similar optical behavior.