Microplankton Discrimination in FlowCAM Images Using Deep Learning

Francisco Bonin-Font, Gorka Buenvaron, Mary Kane, Idan Tuval

2024

Abstract

Marine plankton are omnipresent throughout the oceans, and the Mediterranean Sea is no exception. Innovation on microscopy technology for observing marine plankton over the last several decades has enabled scientist to obtain large quantities of images. While these new instruments permit generating and recording large amounts of visual information about plankton, they have produced a bottleneck and overwhelmed our abilities to provide meaningful taxonomic information quickly. The development of methods based on Artificial Intelligence or Deep Learning to process these images in efficient, cost-effective manners is an active area of continued research. In this study, Convolutional Neural Networks (CNNs) were trained to analyze images of natural assemblages of microplankton (< 100µm) and laboratory monocultures. The CNN configurations and training were focused on differentiating phytoplankton, zooplankton, and zooplankton consuming phytoplankton. Experiments reveal high performance in the discrimination of these different varieties of plankton, in terms of Accuracy, Precision, F1 scores and mean Average Precision.

Paper Citation

in Harvard Style

Bonin-Font F., Buenvaron G., Kane M. and Tuval I. (2024). Microplankton Discrimination in FlowCAM Images Using Deep Learning. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 606-613. DOI: 10.5220/0012460200003660

in Bibtex Style

@conference{visapp24,
author={Francisco Bonin-Font and Gorka Buenvaron and Mary Kane and Idan Tuval},
title={Microplankton Discrimination in FlowCAM Images Using Deep Learning},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={606-613},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012460200003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - Microplankton Discrimination in FlowCAM Images Using Deep Learning
SN - 978-989-758-679-8
AU - Bonin-Font F.
AU - Buenvaron G.
AU - Kane M.
AU - Tuval I.
PY - 2024
SP - 606
EP - 613
DO - 10.5220/0012460200003660
PB - SciTePress