Citation: Wang, Y.; Zhang, L.; Ma, W.;
Wang, Y.; Niu, W.; Song, Y.; Wang, W.
Ocean Plankton Biomass Estimation
with a Digital Holographic
Underwater Glider. J. Mar. Sci. Eng.
2022, 10, 1258. https://doi.org/
10.3390/jmse10091258
Academic Editors: Jacopo Aguzzi,
Giacomo Picardi, Damianos
Chatzievangelou, Simone Marini,
Sascha Flögel, Sergio Stefanni,
Peter Weiss and Daniel Mihai Toma
Received: 11 August 2022
Accepted: 30 August 2022
Published: 6 September 2022
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Journal of
Marine Science
and Engineering
Article
Ocean Plankton Biomass Estimation with a Digital Holographic
Underwater Glider
Yingjie Wang
1
, Lianhong Zhang
1,2,3
, Wei Ma
1,2,3
, Yanhui Wang
1,2,3,
*, Wendong Niu
1,2,3
, Yu Song
3
and Weimin Wang
4
1
Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education,
School of Mechanical Engineering, Tianjin University, Tianjin 300350, China
2
Qingdao Marine Engineering Research Institute of Tianjin University, Tianjin University,
Qingdao 266237, China
3
The Joint Laboratory of Ocean Observing and Detection, Pilot National Laboratory for Marine Science and
Technology, Qingdao 266237, China
4
Deep-Sea Multidisciplinary Research Center, Pilot National Laboratory for Marine Science and Technology,
Qingdao 266237, China
* Correspondence: yanhuiwang@tju.edu.cn
Abstract:
Accurate quantitative plankton observation is significant for biogeochemistry and envi-
ronmental monitoring. However, current observation equipment is mostly shipborne, and there is a
lack of long-term, large-scale, and low-cost methods for plankton observation. This paper proposes
a solution to investigate plankton using a Seascan holographic camera equipped with a “Petrel-II”
underwater glider for a longer time sequence and at a larger scale. Aiming at the new challenges
of low efficiency and low accuracy of holographic image processing after integrating holographic
imaging systems and underwater gliders, a novel plankton data analysis method applicable to
Digital Holographic Underwater Gliders (DHUG) is proposed. The algorithm has the following
features: (1) high efficiency: the algorithm breaks the traditional hologram information extraction
order, focusing only on the key regions in the hologram and minimizing the redundant computation;
(2) high accuracy: applying the Sobel variance algorithm to the plankton in the hologram to focus the
plane extraction significantly improves the focus accuracy; and (3) high degree of automation: by
integrating a convolutional neural network, the algorithm achieves a fully automated analysis of the
observed data. A sea test in the South China Sea verified that the proposed algorithm could greatly
improve the problems of severe plankton segmentation and the low focusing accuracy of traditional
information extraction algorithms. It also proved that the DHUG plankton survey has great potential.
Keywords:
plankton investigation; digital holographic system; background modeling; CNN;
underwater glider
1. Introduction
As primary producers or secondary consumers, plankton are important bait for fish
and other economic animals [
1
], and they play a vital role in the global carbon cycle [
2
,
3
].
Since eutrophication and climate change greatly impact their population, marine scientists
are focusing on the correlation between marine plankton and environmental changes [
4
].
An enhanced understanding of plankton spatio-temporal distribution patterns, mechanisms,
and constraints will facilitate the adoption of an ecological approach and formulate ecosys-
tem protection strategies for climate change adaptation [
5
,
6
]. However, it is difficult to
investigate the spatio-temporal distribution of the plankton community due to its special
living environment. According to traditional methods for plankton investigation, inves-
tigators have mainly adopted Niskin bottles, nets, or plankton pumps to collect samples
in field surveys and bring them back to the laboratory after formalin fixation for manual
J. Mar. Sci. Eng. 2022, 10, 1258. https://doi.org/10.3390/jmse10091258 https://www.mdpi.com/journal/jmse
