Crouzy, BenoîtMeurville, Marie PierreClot, BernardErb, SophieLbadaoui-Darvas, MariaTummon, FionaLieberherr, Gian2025-10-032025-10-032025-10-02202510.1007/s10453-025-09882-w2-s2.0-105016730852https://infoscience.epfl.ch/handle/20.500.14299/254624This note introduces the newly developed MeteoSwiss operational pollen classification model based on digital holography and induced fluorescence measurements. 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.trueAirflow cytometryAutomatic identificationDigital holographyFluorescenceMachine learningPollen monitoringReal-timetext::journal::journal article::research article