Imaging pollen using a Raspberry Pi and LED with deep learning
Imaging pollen using a Raspberry Pi and LED with deep learning
The production of low-cost, small footprint imaging sensor would be invaluable for airborne global monitoring of pollen, which could allow for mitigation of hay fever symptoms. We demonstrate the use of a white light LED (light emitting diode) to illuminate pollen grains and capture their scattering pattern using a Raspberry Pi camera. The scattering patterns are transformed into 20× microscope magnification equivalent images using deep learning. We show the ability to produce images of pollen from plant species previously unseen by the neural network in training. Such a technique could be applied to imaging airborne particulates that contribute to air pollution, and could be used in the field of environmental science, health science and agriculture.
AI, Bioaerosols, Imaging, Palynology, Pollen grains, Sensing
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michalis
1840a474-dd50-4a55-ab74-6f086aa3f701
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
23 October 2024
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michalis
1840a474-dd50-4a55-ab74-6f086aa3f701
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Ben, Zervas, Michalis and Grant-Jacob, James A.
(2024)
Imaging pollen using a Raspberry Pi and LED with deep learning.
Science of the Total Environment, 955, [177084].
(doi:10.1016/j.scitotenv.2024.177084).
Abstract
The production of low-cost, small footprint imaging sensor would be invaluable for airborne global monitoring of pollen, which could allow for mitigation of hay fever symptoms. We demonstrate the use of a white light LED (light emitting diode) to illuminate pollen grains and capture their scattering pattern using a Raspberry Pi camera. The scattering patterns are transformed into 20× microscope magnification equivalent images using deep learning. We show the ability to produce images of pollen from plant species previously unseen by the neural network in training. Such a technique could be applied to imaging airborne particulates that contribute to air pollution, and could be used in the field of environmental science, health science and agriculture.
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STOTEN-D-24-25876-Paper-Corrected-Spaced-Accepted-15102024
- Accepted Manuscript
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1-s2.0-S0048969724072413-main
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Accepted/In Press date: 18 October 2024
Published date: 23 October 2024
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Publisher Copyright:
© 2024 The Authors
Keywords:
AI, Bioaerosols, Imaging, Palynology, Pollen grains, Sensing
Identifiers
Local EPrints ID: 495353
URI: http://eprints.soton.ac.uk/id/eprint/495353
ISSN: 0048-9697
PURE UUID: 58c7585a-1532-42e6-8573-482f91076245
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Date deposited: 11 Nov 2024 18:06
Last modified: 12 Nov 2024 02:46
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Contributors
Author:
Ben Mills
Author:
Michalis Zervas
Author:
James A. Grant-Jacob
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