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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