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Ocean Science An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/os-2020-62
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-2020-62
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  08 Jul 2020

08 Jul 2020

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This preprint is currently under review for the journal OS.

Towards operational phytoplankton recognition with automated high-throughput imaging and compact convolutional neural networks

Tuomas Eerola1, Kaisa Kraft2, Osku Grönberg1, Lasse Lensu1, Sanna Suikkanen2, Jukka Seppälä2, Timo Tamminen2, Heikki Kälviäinen1, and Heikki Haario1 Tuomas Eerola et al.
  • 1Computer Vision and Pattern Recognition Laboratory, School of Engineering Science, Lappeenranta-Lahti University of Technology LUT, Finland
  • 2Finnish Environment Institute, Marine Research Centre, Helsinki, Finland

Abstract. Plankton communities form the basis of aquatic ecosystems and elucidating their role in increasingly important environmental issues is a constantly present research question. The concealed plankton community dynamics reflect changes in environmental forcing, growth traits of competing species, and multiple food web interactions. Recent technological advances have led to the possibility of collecting real-time big data opening new horizons for testing core hypotheses in planktonic systems, derived from macroscopic realms, in community ecology, biodiversity research, and ecosystem functioning. Analyzing the big data calls for computer vision and machine learning methods capable of producing interoperable data across platforms and systems. In this paper we apply convolutional neural networks (CNN) to classify a brackish-water phytoplankton community in the Baltic Sea. For solving the classification task, we utilize compact CNN architectures requiring less computational capacity and creating an opportunity to quickly train the network. This makes it possible to (1) test various modifications to the classification method, and (2) repeat each experiment multiple times with different training and test set combinations to obtain reliable results. We further analyze the effect of large class imbalance to the CNN performance, and test relevant data augmentation techniques to improve the performance. Finally, we address the practical implications of the classification performance to aquatic research by analyzing the confused classes and their effect on the reliability of the automatic plankton recognition system, to guide further development of plankton recognition research. Our results show that it is possible to obtain good classification accuracy with relatively shallow architectures and a small amount of training data when using effective data augmentation methods even with a very unbalanced dataset.

Tuomas Eerola et al.

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Tuomas Eerola et al.

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Short summary
The role of plankton communities in important environmental issues is an active research question. Large amounts of plankton images collected using modern devices call for automated analysis methods. We consider classification of phytoplanktons using compact convolutional neural networks allowing fast model training. We analyse the confused classes and their practical implications to aquatic research. We show that good accuracy can be obtained with a limited amount of unbalanced training data.
The role of plankton communities in important environmental issues is an active research...
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