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

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

© 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation.

Bibliographische Detailangaben
Veröffentlicht in:The New phytologist. - 1979. - 240(2023), 3 vom: 01. Nov., Seite 1305-1326
1. Verfasser: Barnes, Claire M (VerfasserIn)
Weitere Verfasser: Power, Ann L, Barber, Daniel G, Tennant, Richard K, Jones, Richard T, Lee, G Rob, Hatton, Jackie, Elliott, Angela, Zaragoza-Castells, Joana, Haley, Stephen M, Summers, Huw D, Doan, Minh, Carpenter, Anne E, Rees, Paul, Love, John
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2023
Zugriff auf das übergeordnete Werk:The New phytologist
Schlagworte:Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't artificial intelligence deep learning imaging flow cytometry machine learning palaeoecology palynology pollen
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100 1 |a Barnes, Claire M  |e verfasserin  |4 aut 
245 1 0 |a Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry 
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520 |a 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 
650 4 |a Journal Article 
650 4 |a Research Support, N.I.H., Extramural 
650 4 |a Research Support, Non-U.S. Gov't 
650 4 |a artificial intelligence 
650 4 |a deep learning 
650 4 |a imaging flow cytometry 
650 4 |a machine learning 
650 4 |a palaeoecology 
650 4 |a palynology 
650 4 |a pollen 
700 1 |a Power, Ann L  |e verfasserin  |4 aut 
700 1 |a Barber, Daniel G  |e verfasserin  |4 aut 
700 1 |a Tennant, Richard K  |e verfasserin  |4 aut 
700 1 |a Jones, Richard T  |e verfasserin  |4 aut 
700 1 |a Lee, G Rob  |e verfasserin  |4 aut 
700 1 |a Hatton, Jackie  |e verfasserin  |4 aut 
700 1 |a Elliott, Angela  |e verfasserin  |4 aut 
700 1 |a Zaragoza-Castells, Joana  |e verfasserin  |4 aut 
700 1 |a Haley, Stephen M  |e verfasserin  |4 aut 
700 1 |a Summers, Huw D  |e verfasserin  |4 aut 
700 1 |a Doan, Minh  |e verfasserin  |4 aut 
700 1 |a Carpenter, Anne E  |e verfasserin  |4 aut 
700 1 |a Rees, Paul  |e verfasserin  |4 aut 
700 1 |a Love, John  |e verfasserin  |4 aut 
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