Authors:
Francisco Bonin-Font
1
;
Gorka Buenvaron
2
;
Mary Kane
3
and
Idan Tuval
3
Affiliations:
1
Systems, Robotics and Vision Group (SRVG), University of the Balearic Islands, ctra de Valldemossa km 7.5, Palma de Mallorca, Balearic Islands, Spain
;
2
Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), University of the Balearic Islands, ctra de Valldemossa km 7.5, Palma de Mallorca, Balearic Islands, Spain
;
3
Department of Marine Ecology, Mediterranean Institute of Advanced Studies (IMEDEA), Miquel Marqués 21, Esporles, Balearic Islands, Spain
Keyword(s):
Phytoplankton, Zooplankton, Convolutional Neural Networks.
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 performan
ce in the discrimination of these different varieties of plankton,
in terms of Accuracy, Precision, F1 scores and mean Average Precision.
(More)