Operational pollen classification using digital holography and fluorescence

Operational pollen classification using digital holography and fluorescence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Operational pollen classification using digital holography and fluorescence Benoît Crouzy, Marie-Pierre Meurville, Bernard Clot, Sophie Erb, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7158558/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Sep, 2025 Read the published version in Aerobiologia → Version 1 posted 9 You are reading this latest preprint version Abstract This 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 five years of operation of the Swiss automatic pollen monitoring network. pollen monitoring machine learning real-time digital holography fluorescence automatic identification airflow cytometry Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Sep, 2025 Read the published version in Aerobiologia → Version 1 posted Editorial decision: Revision requested 21 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviews received at journal 15 Aug, 2025 Reviewers agreed at journal 03 Aug, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers invited by journal 20 Jul, 2025 Editor assigned by journal 19 Jul, 2025 Submission checks completed at journal 19 Jul, 2025 First submitted to journal 18 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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