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dc.contributor.authorBarnes, CM
dc.contributor.authorPower, AL
dc.contributor.authorBarber, DG
dc.contributor.authorTennant, RK
dc.contributor.authorJones, RT
dc.contributor.authorLee, GR
dc.contributor.authorHatton, J
dc.contributor.authorElliott, A
dc.contributor.authorZaragoza-Castells, J
dc.contributor.authorHaley, SM
dc.contributor.authorSummers, HD
dc.contributor.authorDoan, M
dc.contributor.authorCarpenter, AE
dc.contributor.authorRees, P
dc.contributor.authorLove, J
dc.date.accessioned2023-09-18T14:21:25Z
dc.date.issued2023-09-07
dc.date.updated2023-09-18T13:54:49Z
dc.description.abstractPollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time-consuming task limited in taxonomical precision and sampling frequency, therefore restricting data quality used to inform climate change and pollen forecasting models. We build on the growing work using AI (artificial intelligence) for automated pollen classification to design a flexible network that can deal with the uncertainty of broad-scale environmental applications. We combined imaging flow cytometry with Guided Deep Learning to identify and accurately categorise pollen in environmental samples; here, pollen grains captured within c. 5500 Cal yr BP old lake sediments. Our network discriminates not only pollen included in training libraries to the species level but, depending on the sample, can classify previously unseen pollen to the likely phylogenetic order, family and even genus. Our approach offers valuable insights into the development of a widely transferable, rapid and accurate exploratory tool for pollen classification in 'real-world' environmental samples with improved accuracy over pure deep learning techniques. This work has the potential to revolutionise many aspects of palynology, allowing a more detailed spatial and temporal understanding of pollen in the environment with improved taxonomical resolution.en_GB
dc.description.sponsorshipNational Institutes of Health (NIH)en_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.description.sponsorshipBiotechnology and Biological Sciences Research Council (BBSRC)en_GB
dc.format.mediumPrint-Electronic
dc.identifier.citationPublished online 7 September 2023en_GB
dc.identifier.doihttps://doi.org/10.1111/nph.19186
dc.identifier.grantnumberR35 GM122547en_GB
dc.identifier.grantnumberEP/N013506/1en_GB
dc.identifier.grantnumberBB/P026818/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/134024
dc.identifierORCID: 0000-0003-3033-1858 (Tennant, Richard K)
dc.identifierScopusID: 55450398500 (Tennant, Richard K)
dc.identifierORCID: 0000-0003-0340-7431 (Love, John)
dc.identifierScopusID: 7202207902 (Love, John)
dc.language.isoenen_GB
dc.publisherWiley / New Phytologist Foundationen_GB
dc.relation.urlhttps://www.ebi.ac.uk/biostudies/studies/S-BSST1152en_GB
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pubmed/37678361en_GB
dc.rights© 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.en_GB
dc.subjectartificial intelligenceen_GB
dc.subjectdeep learningen_GB
dc.subjectimaging flow cytometryen_GB
dc.subjectmachine learningen_GB
dc.subjectpalaeoecologyen_GB
dc.subjectpalynologyen_GB
dc.subjectpollenen_GB
dc.titleDeductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometryen_GB
dc.typeArticleen_GB
dc.date.available2023-09-18T14:21:25Z
dc.identifier.issn0028-646X
exeter.place-of-publicationEngland
dc.descriptionThis is the final version. Available on open access from Wiley via the DOI in this recorden_GB
dc.descriptionData availability: The data that support the findings of this study are available from Biostudies: https://www.ebi.ac.uk/biostudies/studies/S-BSST1152 (data embargoed until 1 September 2024).en_GB
dc.identifier.eissn1469-8137
dc.identifier.journalNew Phytologisten_GB
dc.relation.ispartofNew Phytol
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_GB
dcterms.dateAccepted2023-06-30
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2023-09-07
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2023-09-18T14:17:36Z
refterms.versionFCDVoR
refterms.dateFOA2023-09-18T14:21:26Z
refterms.panelAen_GB
refterms.dateFirstOnline2023-09-07


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© 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and
distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Except where otherwise noted, this item's licence is described as © 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.