Deep Learning Classification of Lake Zooplankton

TL;DR

This paper develops deep learning models for automated classification of lake plankton images, achieving high accuracy and outperforming existing models, thereby facilitating real-time ecosystem monitoring.

Contribution

The study introduces transfer learning and ensembling strategies for plankton classification, with a large annotated dataset and models that outperform previous approaches.

Findings

Achieved 98% accuracy and 93% F1 score on lake plankton images.

Models outperformed previous methods on external datasets.

Annotated 17,900 images across 35 classes for model training.

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, FlowCytobot and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.