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In-flight sensing of pollen grains via laser scattering and deep learning

In-flight sensing of pollen grains via laser scattering and deep learning
In-flight sensing of pollen grains via laser scattering and deep learning
The identification and imaging of pollen grains in-flight was performed via illumination of the pollen grains with three collinear laser beams that had central wavelengths of 450 nm, 520 nm and 635 nm. Two neural networks are reported here; the first neural network was able to categorise pollen grain species from their scattering patterns with ~86% accuracy, while the second neural network generated images of the pollen grains from their scattering patterns. This work demonstrates the potential application of laser scattering and deep learning for real-world in-flight pollen identification.
Deep learning, Hay fever, Imaging, Optics, Pollen, Sensing
2631-8695
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

Grant-Jacob, James, Praeger, Matthew, Eason, R.W. and Mills, Benjamin (2021) In-flight sensing of pollen grains via laser scattering and deep learning. Engineering Research Express, 3 (2), [025021]. (doi:10.1088/2631-8695/abfdf8).

Record type: Article

Abstract

The identification and imaging of pollen grains in-flight was performed via illumination of the pollen grains with three collinear laser beams that had central wavelengths of 450 nm, 520 nm and 635 nm. Two neural networks are reported here; the first neural network was able to categorise pollen grain species from their scattering patterns with ~86% accuracy, while the second neural network generated images of the pollen grains from their scattering patterns. This work demonstrates the potential application of laser scattering and deep learning for real-world in-flight pollen identification.

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More information

Accepted/In Press date: 4 May 2021
Published date: 1 June 2021
Additional Information: Funding Information: BM was supported by an EPSRC Early Career Fellowship (EP/N03368X/1) and EPSRC grant (EP/T026197/1). Publisher Copyright: © 2021 The Author(s). Published by IOP Publishing Ltd.
Keywords: Deep learning, Hay fever, Imaging, Optics, Pollen, Sensing

Identifiers

Local EPrints ID: 449196
URI: http://eprints.soton.ac.uk/id/eprint/449196
ISSN: 2631-8695
PURE UUID: b872f3b0-09d2-422a-bf97-7ee8512f98ea
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Matthew Praeger: ORCID iD orcid.org/0000-0002-5814-6155
ORCID for R.W. Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 19 May 2021 18:15
Last modified: 18 Nov 2022 02:40

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