Deep-learning-powered data analysis in plankton ecology

TL;DR

This paper reviews how deep learning techniques are transforming plankton ecology by enabling faster, more objective analysis of plankton images, behaviors, and ecological models, with tutorials and code for practical application.

Contribution

It provides a comprehensive overview of deep learning methods in plankton ecology, highlighting recent advancements, challenges, and future opportunities for research.

Findings

Deep learning improves speed and objectivity in plankton image analysis.

Architectures have evolved to reduce readout imprecision.

Tutorials and code facilitate practical adoption of methods.

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.