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
{"title":"Deep-learning-powered data analysis in plankton ecology","authors":"Harshith Bachimanchi,&nbsp;Matthew I. M. Pinder,&nbsp;Chloé Robert,&nbsp;Pierre De Wit,&nbsp;Jonathan Havenhand,&nbsp;Alexandra Kinnby,&nbsp;Daniel Midtvedt,&nbsp;Erik Selander,&nbsp;Giovanni Volpe","doi":"10.1002/lol2.10392","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":18128,"journal":{"name":"Limnology and Oceanography Letters","volume":null,"pages":null},"PeriodicalIF":5.1000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lol2.10392","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Limnology and Oceanography Letters","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/lol2.10392","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"LIMNOLOGY","Score":null,"Total":0}
引用次数: 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 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.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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.
期刊最新文献
Disentangling effects of droughts and heatwaves on alpine periphyton communities: A mesocosm experiment Snow removal cools a small dystrophic lake Unraveling Lake Geneva's hypoxia crisis in the Anthropocene Simple visualization of fish migration history based on high‐resolution otolith δ18O profiles and hydrodynamic models Arctic fishes reveal patterns in radiocarbon age across habitats and with recent climate change
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1