Deep-learning-powered data analysis in plankton ecology

IF 5.1 2区 地球科学 Q1 LIMNOLOGY Limnology and Oceanography Letters Pub Date : 2024-04-18 DOI:10.1002/lol2.10392
Harshith Bachimanchi, Matthew I. M. Pinder, Chloé Robert, Pierre De Wit, Jonathan Havenhand, Alexandra Kinnby, Daniel Midtvedt, Erik Selander, Giovanni Volpe
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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 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.

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浮游生物生态学中由深度学习驱动的数据分析
深度学习算法的应用为浮游生物生态学带来了新的视角。作为既有方法的替代方法,深度学习为研究不同环境中的浮游生物提供了客观方案。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,以及生态建模。深度学习有可能加快分析速度,减少人为实验偏差,从而在相关的时间和空间尺度上获取数据,提高可重复性。我们还讨论了不足之处,并展示了深度学习架构是如何发展以减少不精确读数的。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,读者可以将本综述中介绍的方法应用到自己的数据中。
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来源期刊
CiteScore
10.00
自引率
3.80%
发文量
63
审稿时长
25 weeks
期刊介绍: Limnology and Oceanography Letters (LO-Letters) serves as a platform for communicating the latest innovative and trend-setting research in the aquatic sciences. Manuscripts submitted to LO-Letters are expected to present high-impact, cutting-edge results, discoveries, or conceptual developments across all areas of limnology and oceanography, including their integration. Selection criteria for manuscripts include their broad relevance to the field, strong empirical and conceptual foundations, succinct and elegant conclusions, and potential to advance knowledge in aquatic sciences.
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