Deep-learning-powered data analysis in plankton ecology
(2024) In Limnology and Oceanography Letters- Abstract
The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phytoplankton and zooplankton images, foraging and swimming behavior analysis, and finally ecological modeling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved... (More)
The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phytoplankton and zooplankton images, foraging and swimming behavior analysis, and finally ecological modeling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.
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- author
- Bachimanchi, Harshith ; Pinder, Matthew I.M. ; Robert, Chloé ; De Wit, Pierre ; Havenhand, Jonathan ; Kinnby, Alexandra ; Midtvedt, Daniel ; Selander, Erik LU and Volpe, Giovanni
- organization
- publishing date
- 2024-04-18
- type
- Contribution to journal
- publication status
- epub
- subject
- in
- Limnology and Oceanography Letters
- pages
- 16 pages
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- scopus:85191008625
- ISSN
- 2378-2242
- DOI
- 10.1002/lol2.10392
- language
- English
- LU publication?
- yes
- id
- 4a9b9133-b580-4ef9-af15-9841b45d64cc
- date added to LUP
- 2024-05-07 15:33:55
- date last changed
- 2024-05-13 14:44:10
@article{4a9b9133-b580-4ef9-af15-9841b45d64cc,
abstract = {{<p>The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phytoplankton and zooplankton images, foraging and swimming behavior analysis, and finally ecological modeling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.</p>}},
author = {{Bachimanchi, Harshith and Pinder, Matthew I.M. and Robert, Chloé and De Wit, Pierre and Havenhand, Jonathan and Kinnby, Alexandra and Midtvedt, Daniel and Selander, Erik and Volpe, Giovanni}},
issn = {{2378-2242}},
language = {{eng}},
month = {{04}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Limnology and Oceanography Letters}},
title = {{Deep-learning-powered data analysis in plankton ecology}},
url = {{http://dx.doi.org/10.1002/lol2.10392}},
doi = {{10.1002/lol2.10392}},
year = {{2024}},
}