Monitoring of Plankton Spatial and Temporal Characteristics With the Use of a Submersible Digital Holographic Camera

The study shows that the use of a submersible digital holographic camera as part of a multifunctional hardware and software complex allows carrying out in situ measurements of plankton, automating the process of obtaining data on plankton, as well as classifying plankton species up to an order withi...

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Bibliographic Details
Published in:Frontiers in Marine Science 2020-08, Vol.7
Main Authors: Dyomin, Victor, Davydova, Alexandra, Morgalev, Sergey, Kirillov, Nikolay, Olshukov, Alexey, Polovtsev, Igor, Davydov, Sergey
Format: Article
Language:English
Subjects:
Algorithms
Automatic classification
Automation
Biodiversity
Cameras
Classification
Decision trees
digital holography
Environmental monitoring
In situ measurement
Laboratories
Morphology
Plankton
Profiles
Software
spatial characteristics
Species diversity
submersible camera
Submersibles
temporal characteristics
Vertical profiles
Water area
Zooplankton
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Summary:The study shows that the use of a submersible digital holographic camera as part of a multifunctional hardware and software complex allows carrying out in situ measurements of plankton, automating the process of obtaining data on plankton, as well as classifying plankton species up to an order within the specified taxonomic groups. Such automation ensures monitoring expeditionary or stationary research of species diversity and spatial and temporal organization of zooplankton in conjunction with hydrophysical parameters of the medium. This paper presents the full-scale results of vertical profiles and daily measurements of plankton made with the use of the submersible digital holographic camera, as well as the classification of plankton in laboratory and field conditions in the automatic mode. It is shown that within the accomplished version the classification algorithm using the morphological parameter makes it possible to solve the problem quickly (time required to obtain the result is less than 1 second and depends on the number of plankton particles and the frame size of a restored image), however the classification accuracy by orders varies within 50-60%.
ISSN:2296-7745
2296-7745
DOI:10.3389/fmars.2020.00653