Operational pollen classification using digital holography and fluorescence
Year: 2025
Type: article
Abstract: This note introduces the newly developed MeteoSwiss operational pollen classification model based on digital holography and induced fluorescencemeasurements. A targeted selection of curated training datasets together with a revised model architecture result in considerable improvements compared to previous operational model. The new classification model, which has been trained specifically for Switzerland, is provided openly for use in a standard format for machine learning interoperability. In addition to the description of the new classification model, we motivate the need for this development by presenting the most significant issue met during the first 5 years of operation of the Swiss automatic pollen monitoring network. The authors acknowledge supportof the following research funding: EU Horizon projectSYLVA (Grant nbr 101086109) and 23NRM03BioAirMet Project (BioAirMet - bioAirmet.ptb.de)). Theproject 23NRM03 BioAirMet has received funding from theEuropean Partnership on Metrology, co-financed from theEuropean Union's Horizon Europe Research and InnovationProgramme and by the Participating States. The presentmanuscript is a contribution to the EUMETNET AutoPollenProgram. Sophie Erb was funded by the Swiss NationalScience Foundation (IZCOZ0_198117). We are grateful toJulia Burkard for providing Fagus pollen data from theSwisensPoleno Jupiter from the University of Vienna and toSwisens AG for sharing their model training pipeline
Author: Crouzy, Benoit
Language: English
Subfield: Immunology and Allergy
Field: Medicine
Domain: Health Sciences
Open Access status: green
Award: G2754286174
OpenAlex ID: W7108210763
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