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.

中文翻译：

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浮游生物生态学中深度学习驱动的数据分析

深度学习算法的实现给浮游生物生态学带来了新的视角。作为现有方法的替代方法，深度学习提供了研究不同环境中浮游生物的客观方案。我们概述了基于深度学习的方法，包括浮游植物和浮游动物图像的检测和分类、觅食和游泳行为分析，以及最后的生态建模。深度学习有潜力加快分析速度并减少人类实验偏差，从而能够在相关时间和空间尺度上获取数据，并提高可重复性。我们还讨论了缺点并展示了深度学习架构如何发展以减少不精确的读数。最后，我们提出了深度学习特别有可能促进浮游生物研究的机会。这些示例附有详细的教程和代码示例，允许读者将本评论中描述的方法应用于自己的数据。