Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps Article Swipe
Ingrida Šaulienė
,
Laura Šukienė
,
Gintautas Daunys
,
Gediminas Valiulis
,
Lukas Vaitkevičius
,
Predrag Matavulj
,
Sanja Brdar
,
Marko Panić
,
Branko Šikoparija
,
Bernard Clot
,
Benoît Crouzy
,
Mikhail Sofiev
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.5194/amt-2018-432
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.5194/amt-2018-432
Pollen-induced allergy is among the most-prevalent non-contagious diseases, with about a quarter of European population sensitive to various atmospheric bioaerosols. In most European countries, pollen information is based on a weekly-cycle Hirst-type pollen trap method. This method is labour-intensive, requires narrow specialization abilities and substantial time, so that the pollen data are always delayed, subject to sampling- and counting-related uncertainties. Emerging new approaches to automatic pollen monitoring can, in principle, allow for real-time availability of the data with no human involvement. The goal of the current paper is to evaluate the capabilities of the new Plair Rapid-E pollen monitor and to construct the first-level pollen recognition algorithm. The evaluation was performed for three devices located in Lithuania, Serbia and Switzerland, with independent calibration data and classification algorithms. The Rapid-E output data include multi-angle scattering images and the fluorescence spectra recorded at several times for each particle reaching the device. Both modalities of the Rapid-E output were treated with artificial neural networks (ANN) and the results were combined to obtain the pollen type. For the first classification experiment, the monitor was challenged with a large variety of pollen types and the quality of many-to-many classification was evaluated. It was shown that in this case, both scattering- and fluorescence- based recognition algorithms fall short of acceptable quality. The combinations of these algorithms performed better exceeding 80 % accuracy for 5 out of 11 species. Fluorescence spectra showed similarities among different species ending up with three well-resolved groups: (Alnus, Corylus, Betula and Quercus), (Salix and Populus), and (Festuca, Artemisia, Juniperus). Within these groups, pollen is practically non-distinguishable for the first-level recognition procedure. Construction of multi-steps algorithms with sequential discrimination of pollen inside each group seems to be one of possible ways forwards. In order to connect the classification experiment to existing technology, a short comparison with the Hirst measurements is presented and an issue of the false-positive pollen detections by Rapid-E is discussed.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/amt-2018-432
- OA Status
- gold
- Cited By
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4233762152
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4233762152Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/amt-2018-432Digital Object Identifier
- Title
-
Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next stepsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-21Full publication date if available
- Authors
-
Ingrida Šaulienė, Laura Šukienė, Gintautas Daunys, Gediminas Valiulis, Lukas Vaitkevičius, Predrag Matavulj, Sanja Brdar, Marko Panić, Branko Šikoparija, Bernard Clot, Benoît Crouzy, Mikhail SofievList of authors in order
- Landing page
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https://doi.org/10.5194/amt-2018-432Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5194/amt-2018-432Direct OA link when available
- Concepts
-
Pollen, Aerobiology, Computer science, Construct (python library), Indoor bioaerosol, Population, Artificial intelligence, Algorithm, Machine learning, Pattern recognition (psychology), Geography, Biology, Botany, Meteorology, Medicine, Environmental health, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2022: 3, 2020: 3, 2019: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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