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Operational pollen classification using digital holography and fluorescence.

Benoît Crouzy1, Marie-Pierre Meurville1, Bernard Clot1

  • 1Surface Measurements, MeteoSwiss, Chemin de l'Aérologie, 1530 Payerne, Switzerland.

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Summary

MeteoSwiss has a new pollen classification model using digital holography and fluorescence. This improved model, trained for Switzerland, is now openly available for machine learning applications.

Keywords:
Airflow cytometryAutomatic identificationDigital holographyFluorescenceMachine learningPollen monitoringReal-time

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Area of Science:

  • Environmental Science
  • Biotechnology
  • Computer Science

Background:

  • The Swiss automatic pollen monitoring network faced significant operational challenges in its first five years.
  • Existing pollen classification models required enhancement to meet operational demands.

Purpose of the Study:

  • To introduce a newly developed operational pollen classification model for MeteoSwiss.
  • To address the limitations of previous models and improve pollen monitoring accuracy.
  • To provide an interoperable machine learning model for broader use.

Main Methods:

  • Utilized digital holography and induced fluorescence measurements for pollen analysis.
  • Developed a revised model architecture with targeted, curated training datasets.
  • Ensured the model is provided in a standard format for machine learning interoperability.

Main Results:

  • The new classification model demonstrates considerable improvements over previous versions.
  • The model is specifically trained and optimized for the Swiss environment.
  • The open availability of the model facilitates wider adoption and research.

Conclusions:

  • The enhanced pollen classification model offers improved accuracy and operational efficiency for Switzerland.
  • The open-source nature of the model promotes advancements in aerobiology and machine learning applications.
  • Addressing past operational issues has led to a more robust and reliable pollen monitoring system.