Articles | Volume 17, issue 2
https://doi.org/10.5194/amt-17-441-2024
https://doi.org/10.5194/amt-17-441-2024
Research article
 | 
23 Jan 2024
Research article |  | 23 Jan 2024

Real-time pollen identification using holographic imaging and fluorescence measurements

Sophie Erb, Elias Graf, Yanick Zeder, Simone Lionetti, Alexis Berne, Bernard Clot, Gian Lieberherr, Fiona Tummon, Pascal Wullschleger, and Benoît Crouzy

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Cited articles

Adamov, S., Lemonis, N., Clot, B., Crouzy, B., Gehrig, R., Graber, M. J., Sallin, C., and Tummon, F.: On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers, Aerobiologia, 1–15, https://doi.org/10.1007/s10453-021-09724-5, 2021. 
Beggs, P. J.: Impacts of climate change on allergens and allergic diseases, Cambridge University Press, https://doi.org/10.1017/CBO9781107272859, 2016. 
Chappuis, C., Tummon, F., Clot, B., Konzelmann, T., Calpini, B., and Crouzy, B.: Automatic pollen monitoring: first insights from hourly data, Aerobiologia, 36, 159–170, https://doi.org/10.1007/s10453-019-09619-6, 2020. 
Chollet, F.: Keras, GitHub [code], https://github.com/fchollet/keras (last access: 22 April 2023), 2015. 
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In this study, we focus on an automatic bioaerosol measurement instrument and investigate the impact of using its fluorescence measurement for pollen identification. The fluorescence signal is used together with a pair of images from the same instrument to identify single pollen grains via neural networks. We test whether considering fluorescence as a supplementary input improves the pollen identification performance by comparing three different neural networks.
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