Deep-learning-powered data analysis in plankton ecology
This is an incremental review that summarizes existing deep learning applications for plankton researchers, offering tutorials and code for practical use.
This paper reviews how deep learning algorithms can be used to analyze plankton ecology, tackling problems like detection, classification, and behavior analysis, with the potential to speed up analysis and reduce human bias, though no concrete numerical results are provided.
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.