Deep-learning-powered data analysis in plankton ecology
- URL: http://arxiv.org/abs/2309.08500v1
- Date: Fri, 15 Sep 2023 16:04:11 GMT
- Title: Deep-learning-powered data analysis in plankton ecology
- Authors: Harshith Bachimanchi, Matthew I.M. Pinder, Chlo\'e Robert, Pierre De
Wit, Jonathan Havenhand, Alexandra Kinnby, Daniel Midtvedt, Erik Selander,
and Giovanni Volpe
- Abstract summary: The implementation of deep learning algorithms has brought new perspectives to plankton ecology.
Deep learning offers objective schemes to investigate plankton organisms in diverse environments.
- Score: 31.874825130479174
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 phyto-
and zooplankton images, foraging and swimming behaviour analysis, and finally
ecological modelling. 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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