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An operational robotic pollen monitoring network based on automatic image recognition.

Authors
  • Oteros, Jose1
  • Weber, Alisa2
  • Kutzora, Suzanne2
  • Rojo, Jesús3
  • Heinze, Stefanie2
  • Herr, Caroline2
  • Gebauer, Robert4
  • Schmidt-Weber, Carsten B5
  • Buters, Jeroen T M6
  • 1 Center of Allergy & Environment (ZAUM), Member of the German Center for Lung Research (DZL), Technische Universität München/Helmholtz Center, Munich, Germany; Department of Botany, Ecology and Plant Physiology, University of Córdoba, Córdoba, Spain. , (Germany)
  • 2 Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit (LGL), Munich, Germany. , (Germany)
  • 3 Center of Allergy & Environment (ZAUM), Member of the German Center for Lung Research (DZL), Technische Universität München/Helmholtz Center, Munich, Germany; Department of Environmental Sciences, University of Castilla La-Mancha, Toledo, Spain. , (Germany)
  • 4 IT Consulting Robert Gebauer, Germany. , (Germany)
  • 5 Center of Allergy & Environment (ZAUM), Member of the German Center for Lung Research (DZL), Technische Universität München/Helmholtz Center, Munich, Germany. , (Germany)
  • 6 Center of Allergy & Environment (ZAUM), Member of the German Center for Lung Research (DZL), Technische Universität München/Helmholtz Center, Munich, Germany. Electronic address: [email protected] , (Germany)
Type
Published Article
Journal
Environmental Research
Publisher
Elsevier
Publication Date
Aug 16, 2020
Volume
191
Pages
110031–110031
Identifiers
DOI: 10.1016/j.envres.2020.110031
PMID: 32814105
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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. Copyright © 2020 Elsevier Inc. All rights reserved.

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