An operational robotic pollen monitoring network based on automatic image recognition
Autor
Oteros, José
Weber, Alisa
Kutzora, Suzanne
Rojo, Jesús
Heinze, Stefanie
Herr, Caroline
Gebauer, Robert
Schmidt-Weber, Carsten
Buters, Jeroen
Editor
ElsevierFecha
2020Materia
AllergyAerobiology
BAA500
Hirst
ePIN
Quality control
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There is high demand for online, real-time and high-quality pollen data. To the moment pollen monitoring has
been done manually by highly specialized experts.
Here we evaluate the electronic Pollen Information Network (ePIN) comprising 8 automatic BAA500 pollen
monitors in Bavaria, Germany. Automatic BAA500 and manual Hirst-type pollen traps were run simultaneously
at the same locations for one pollen season. Classifications by BAA500 were checked by experts in pollen
identification, which is traditionally considered to be the “gold standard” for pollen monitoring.
BAA500 had a multiclass accuracy of over 90%. Correct identification of any individual pollen taxa was always
>85%, except for Populus (73%) and Alnus (64%). The BAA500 was more precise than the manual method, with
less discrepancies between determinations by pairs of automatic pollen monitors than between pairs of humans.
The BAA500 was online for 97% of the time. There was a significant correlation of 0.84 between airborne pollen
concentrations from the BAA500 and Hirst-type pollen traps. Due to the lack of calibration samples it is unknown
which instrument gives the true concentration.
The automatic BAA500 network delivered pollen data rapidly (3 h delay with real-time), reliably and online.
We consider the ability to retrospectively check the accuracy of the reported classification essential for any
automatic system.