No Cover Image

Journal article 36 views 2 downloads

Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry

Claire Barnes Orcid Logo, Ann L. Power Orcid Logo, Daniel G. Barber Orcid Logo, Richard K. Tennant Orcid Logo, Richard T. Jones, G. Rob Lee, Jackie Hatton, Angela Elliott, Joana Zaragoza‐Castells, Stephen M. Haley Orcid Logo, Huw Summers Orcid Logo, Minh Doan Orcid Logo, Anne E. Carpenter Orcid Logo, Paul Rees Orcid Logo, John Love Orcid Logo

New Phytologist, Volume: 240, Issue: 3, Pages: 1305 - 1326

Swansea University Authors: Claire Barnes Orcid Logo, Huw Summers Orcid Logo, Paul Rees Orcid Logo

  • 68203.VoR.pdf

    PDF | Version of Record

    Copyright 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License.

    Download (6.33MB)

Check full text

DOI (Published version): 10.1111/nph.19186

Abstract

Pollen 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 use...

Full description

Published in: New Phytologist
ISSN: 0028-646X 1469-8137
Published: Wiley 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa68203
first_indexed 2024-11-25T14:21:38Z
last_indexed 2025-01-16T20:49:09Z
id cronfa68203
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2025-01-16T14:38:31.8913673</datestamp><bib-version>v2</bib-version><id>68203</id><entry>2024-11-06</entry><title>Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry</title><swanseaauthors><author><sid>024232879fc13d5ceac584360af8742c</sid><ORCID>0000-0003-1031-7127</ORCID><firstname>Claire</firstname><surname>Barnes</surname><name>Claire Barnes</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>a61c15e220837ebfa52648c143769427</sid><ORCID>0000-0002-0898-5612</ORCID><firstname>Huw</firstname><surname>Summers</surname><name>Huw Summers</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>537a2fe031a796a3bde99679ee8c24f5</sid><ORCID>0000-0002-7715-6914</ORCID><firstname>Paul</firstname><surname>Rees</surname><name>Paul Rees</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-11-06</date><deptcode>EAAS</deptcode><abstract>Pollen 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 &#x2018;real-world&#x2019; 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.</abstract><type>Journal Article</type><journal>New Phytologist</journal><volume>240</volume><journalNumber>3</journalNumber><paginationStart>1305</paginationStart><paginationEnd>1326</paginationEnd><publisher>Wiley</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0028-646X</issnPrint><issnElectronic>1469-8137</issnElectronic><keywords>artificial intelligence; deep learnin; imaging flow cytometry; machine learning; palaeoecology; palynology; pollen</keywords><publishedDay>1</publishedDay><publishedMonth>11</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-11-01</publishedDate><doi>10.1111/nph.19186</doi><url/><notes/><college>COLLEGE NANME</college><department>Engineering and Applied Sciences School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>EAAS</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>NEUBIAS. Grant Number: ECOST-STSM-Request-CA15124-43471 NIH Clinical Center. Grant Number: R35 GM122547 UK Biotechnology and Biological Sciences Research Council. Grant Number: BB/P026818/1 UK Engineering and Physical Sciences Research Council. Grant Number: EP/N013506/1</funders><projectreference/><lastEdited>2025-01-16T14:38:31.8913673</lastEdited><Created>2024-11-06T19:32:45.7492536</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Biomedical Engineering</level></path><authors><author><firstname>Claire</firstname><surname>Barnes</surname><orcid>0000-0003-1031-7127</orcid><order>1</order></author><author><firstname>Ann L.</firstname><surname>Power</surname><orcid>0000-0002-7651-5276</orcid><order>2</order></author><author><firstname>Daniel G.</firstname><surname>Barber</surname><orcid>0000-0002-5022-2846</orcid><order>3</order></author><author><firstname>Richard K.</firstname><surname>Tennant</surname><orcid>0000-0003-3033-1858</orcid><order>4</order></author><author><firstname>Richard T.</firstname><surname>Jones</surname><order>5</order></author><author><firstname>G. Rob</firstname><surname>Lee</surname><order>6</order></author><author><firstname>Jackie</firstname><surname>Hatton</surname><order>7</order></author><author><firstname>Angela</firstname><surname>Elliott</surname><order>8</order></author><author><firstname>Joana</firstname><surname>Zaragoza&#x2010;Castells</surname><order>9</order></author><author><firstname>Stephen M.</firstname><surname>Haley</surname><orcid>0000-0002-5928-3063</orcid><order>10</order></author><author><firstname>Huw</firstname><surname>Summers</surname><orcid>0000-0002-0898-5612</orcid><order>11</order></author><author><firstname>Minh</firstname><surname>Doan</surname><orcid>0000-0002-3235-0457</orcid><order>12</order></author><author><firstname>Anne E.</firstname><surname>Carpenter</surname><orcid>0000-0003-1555-8261</orcid><order>13</order></author><author><firstname>Paul</firstname><surname>Rees</surname><orcid>0000-0002-7715-6914</orcid><order>14</order></author><author><firstname>John</firstname><surname>Love</surname><orcid>0000-0003-0340-7431</orcid><order>15</order></author></authors><documents><document><filename>68203__33358__5d0a2bda2ebf4ebaa55c8e927c2ca1c8.pdf</filename><originalFilename>68203.VoR.pdf</originalFilename><uploaded>2025-01-16T14:34:26.8418050</uploaded><type>Output</type><contentLength>6634079</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by-nc-nd/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2025-01-16T14:38:31.8913673 v2 68203 2024-11-06 Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry 024232879fc13d5ceac584360af8742c 0000-0003-1031-7127 Claire Barnes Claire Barnes true false a61c15e220837ebfa52648c143769427 0000-0002-0898-5612 Huw Summers Huw Summers true false 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false 2024-11-06 EAAS Pollen 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. Journal Article New Phytologist 240 3 1305 1326 Wiley 0028-646X 1469-8137 artificial intelligence; deep learnin; imaging flow cytometry; machine learning; palaeoecology; palynology; pollen 1 11 2023 2023-11-01 10.1111/nph.19186 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University Another institution paid the OA fee NEUBIAS. Grant Number: ECOST-STSM-Request-CA15124-43471 NIH Clinical Center. Grant Number: R35 GM122547 UK Biotechnology and Biological Sciences Research Council. Grant Number: BB/P026818/1 UK Engineering and Physical Sciences Research Council. Grant Number: EP/N013506/1 2025-01-16T14:38:31.8913673 2024-11-06T19:32:45.7492536 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Claire Barnes 0000-0003-1031-7127 1 Ann L. Power 0000-0002-7651-5276 2 Daniel G. Barber 0000-0002-5022-2846 3 Richard K. Tennant 0000-0003-3033-1858 4 Richard T. Jones 5 G. Rob Lee 6 Jackie Hatton 7 Angela Elliott 8 Joana Zaragoza‐Castells 9 Stephen M. Haley 0000-0002-5928-3063 10 Huw Summers 0000-0002-0898-5612 11 Minh Doan 0000-0002-3235-0457 12 Anne E. Carpenter 0000-0003-1555-8261 13 Paul Rees 0000-0002-7715-6914 14 John Love 0000-0003-0340-7431 15 68203__33358__5d0a2bda2ebf4ebaa55c8e927c2ca1c8.pdf 68203.VoR.pdf 2025-01-16T14:34:26.8418050 Output 6634079 application/pdf Version of Record true Copyright 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License. true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry
spellingShingle Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry
Claire Barnes
Huw Summers
Paul Rees
title_short Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry
title_full Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry
title_fullStr Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry
title_full_unstemmed Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry
title_sort Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry
author_id_str_mv 024232879fc13d5ceac584360af8742c
a61c15e220837ebfa52648c143769427
537a2fe031a796a3bde99679ee8c24f5
author_id_fullname_str_mv 024232879fc13d5ceac584360af8742c_***_Claire Barnes
a61c15e220837ebfa52648c143769427_***_Huw Summers
537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees
author Claire Barnes
Huw Summers
Paul Rees
author2 Claire Barnes
Ann L. Power
Daniel G. Barber
Richard K. Tennant
Richard T. Jones
G. Rob Lee
Jackie Hatton
Angela Elliott
Joana Zaragoza‐Castells
Stephen M. Haley
Huw Summers
Minh Doan
Anne E. Carpenter
Paul Rees
John Love
format Journal article
container_title New Phytologist
container_volume 240
container_issue 3
container_start_page 1305
publishDate 2023
institution Swansea University
issn 0028-646X
1469-8137
doi_str_mv 10.1111/nph.19186
publisher Wiley
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
document_store_str 1
active_str 0
description Pollen 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.
published_date 2023-11-01T20:44:57Z
_version_ 1821983320413569024
score 11.048042

Similar Items