Deep Learning Classification of Lake Zooplankton

Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communitie...

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Autores principales:	Sreenath P. Kyathanahally, Thomas Hardeman, Ewa Merz, Thea Bulas, Marta Reyes, Peter Isles, Francesco Pomati, Marco Baity-Jesi
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	plankton camera deep learning plankton classification transfer learning Greifensee ensemble learning Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/3e20fa8149824e41b8e6f4721ec9e236
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id	oai:doaj.org-article:3e20fa8149824e41b8e6f4721ec9e236
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spelling	oai:doaj.org-article:3e20fa8149824e41b8e6f4721ec9e2362021-11-15T05:53:23ZDeep Learning Classification of Lake Zooplankton1664-302X10.3389/fmicb.2021.746297https://doaj.org/article/3e20fa8149824e41b8e6f4721ec9e2362021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmicb.2021.746297/fullhttps://doaj.org/toc/1664-302XPlankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances, which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, Imaging FlowCytobot, and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.Sreenath P. KyathanahallyThomas HardemanEwa MerzThea BulasMarta ReyesPeter IslesFrancesco PomatiMarco Baity-JesiFrontiers Media S.A.articleplankton cameradeep learningplankton classificationtransfer learningGreifenseeensemble learningMicrobiologyQR1-502ENFrontiers in Microbiology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	plankton camera deep learning plankton classification transfer learning Greifensee ensemble learning Microbiology QR1-502
spellingShingle	plankton camera deep learning plankton classification transfer learning Greifensee ensemble learning Microbiology QR1-502 Sreenath P. Kyathanahally Thomas Hardeman Ewa Merz Thea Bulas Marta Reyes Peter Isles Francesco Pomati Marco Baity-Jesi Deep Learning Classification of Lake Zooplankton
description	Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances, which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, Imaging FlowCytobot, and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.
format	article
author	Sreenath P. Kyathanahally Thomas Hardeman Ewa Merz Thea Bulas Marta Reyes Peter Isles Francesco Pomati Marco Baity-Jesi
author_facet	Sreenath P. Kyathanahally Thomas Hardeman Ewa Merz Thea Bulas Marta Reyes Peter Isles Francesco Pomati Marco Baity-Jesi
author_sort	Sreenath P. Kyathanahally
title	Deep Learning Classification of Lake Zooplankton
title_short	Deep Learning Classification of Lake Zooplankton
title_full	Deep Learning Classification of Lake Zooplankton
title_fullStr	Deep Learning Classification of Lake Zooplankton
title_full_unstemmed	Deep Learning Classification of Lake Zooplankton
title_sort	deep learning classification of lake zooplankton
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/3e20fa8149824e41b8e6f4721ec9e236
work_keys_str_mv	AT sreenathpkyathanahally deeplearningclassificationoflakezooplankton AT thomashardeman deeplearningclassificationoflakezooplankton AT ewamerz deeplearningclassificationoflakezooplankton AT theabulas deeplearningclassificationoflakezooplankton AT martareyes deeplearningclassificationoflakezooplankton AT peterisles deeplearningclassificationoflakezooplankton AT francescopomati deeplearningclassificationoflakezooplankton AT marcobaityjesi deeplearningclassificationoflakezooplankton
_version_	1718428546117926912

Deep Learning Classification of Lake Zooplankton

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