Deep Learning Classification of Lake Zooplankton

• Main Authors: Kyathanahally, S. P, Hardeman, T, Merz, E, Kozakiewicz, T, Reyes, M, Isles, P, Pomati, F, Baity-Jesi, M
• Format: Journal Article
• Language: English
• Published: 11-08-2021
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Quantitative Biology - Populations and Evolution
• DOI: 10.48550/arxiv.2108.05258

Front. Microbiol. 12:746297 (2021) 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, FlowCytobot and ISIIS), our models performed better than previously
used models. Our annotated data, code and classification models are freely
available online.