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

Abstract

This 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 the 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 (the time required to obtain the result is less than 1 s 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%.

Phototropic response features for different systematic groups of mesoplankton under adverse environmental conditions

“…The DHC allows registering a digital hologram of the entire studied volume of water with plankton per one exposure (laser pulse) and then restoring the image of this volume in a layers‐by‐layers mode. The software‐based DHC technology for digital hologram recording and processing allows automatically restoring the spatial distribution of particles in the studied volume (3D coordinates of each particle), determining the size, shape, speed, and direction of movement of each particle, and recognizing them (Dyomin, Davydova, et al., 2020; Dyomin, Davydova, Morgalev, Olshukov, et al., 2019; Dyomin, Gribenyukov, Davydova, et al., 2019; Dyomin et al., 2018; Dyomin, Polovtsev, Davydova, & Olshukov, 2019). Besides, it registers individuals with the minimum size of 100 μm at the resolution sufficient enough to identify plankton individuals.…”

“…The equipment created at Tomsk State University (TSU) (digital holographic cameras and hydrobiological probes based on them) (Dyomin, Davydova, et al., 2020; Dyomin, Gribenyukov, Davydova, et al., 2019; Dyomin et al., 2018, 2019) also provides measurements of individual particles, but differs from known analogues in the possibility of photostimulation with attracting radiation causing a phototropic response of zooplankton (Dyomin, Davydova, Morgalev, Olshukov, et al., 2019). The advantages include both a large size of the controlled volume and thus obtained representativeness of data.…”

A Review of Holography in the Aquatic Sciences: In situ Characterization of Particles, Plankton, and Small Scale Biophysical Interactions

“…Bochdansky et al (2013) developed the first deep-sea holographic microscope rated to 6000 m water depth. Other successfully deployed in situ holographic sensors with different optical setups and varying sampling parameters include those by Pfitsch et al (2005Pfitsch et al ( , 2007, Sun et al (2008), Graham et al (2012), Talapatra et al (2012Talapatra et al ( , 2013, Dyomin et al (2019Dyomin et al ( , 2020, and . Table 1 provides further details on some selected systems.…”

Underwater Holographic Sensor for Plankton Studies In Situ including Accompanying Measurements

“…To describe plankton particles within taxa it is necessary to ensure their automatic recognition. Usually [21] we use eight main taxa for ecosystem classification: Chaetognatha, Phytoplankton colony, Phytoplankton chain, Copepoda, Copelata, Rotifera, Cladocera, and Other. This composition of plankton is chosen based on their greatest prevalence in the world ocean.…”

“…The values of taxonomic features obtained through the analysis of plankton images from marine species identification databases [45,46] are tested on plankton images reconstructed from digital holograms and are given in [47]. As the experience of applying the automatic classification [21,35,39,40] has shown, the decision tree may be changed, for example, a new feature may be added-compactness [48], which may be expressed by the ratio of the area to the length of the particle image boundary. Such a possibility may be useful for more detailed or accurate classification, for example to diagnose gas hydrate methane bubbles or oil droplets.…”