Deep Learning Classification of Lake Zooplankton
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Date
2021-11-15
Publication Type
Journal Article
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yes
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Abstract
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.
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Publication status
published
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Book title
Journal / series
Volume
12
Pages / Article No.
746297
Publisher
Frontiers Media
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Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
plankton camera; deep learning; lake plankton; Plankton classification; zooplankton images; transfer learning; Greifensee; ensemble learning; machine learning; fresh water; lake plankton images
Organisational unit
03705 - Jokela, Jukka / Jokela, Jukka