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Dyomin V, Davydova A, Kirillov N, Kondratova O, Morgalev Y, Morgalev S, Morgaleva T, Polovtsev I. Monitoring Bioindication of Plankton through the Analysis of the Fourier Spectra of the Underwater Digital Holographic Sensor Data. SENSORS (BASEL, SWITZERLAND) 2024; 24:2370. [PMID: 38610582 PMCID: PMC11014362 DOI: 10.3390/s24072370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 03/29/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024]
Abstract
The study presents a bioindication complex and a technology of the experiment based on a submersible digital holographic camera with advanced monitoring capabilities for the study of plankton and its behavioral characteristics in situ. Additional mechanical and software options expand the capabilities of the digital holographic camera, thus making it possible to adapt the depth of the holographing scene to the parameters of the plankton habitat, perform automatic registration of the "zero" frame and automatic calibration, and carry out natural experiments with plankton photostimulation. The paper considers the results of a long-term digital holographic experiment on the biotesting of the water area in Arctic latitudes. It shows additional possibilities arising during the spectral processing of long time series of plankton parameters obtained during monitoring measurements by a submersible digital holographic camera. In particular, information on the rhythmic components of the ecosystem and behavioral characteristics of plankton, which can be used as a marker of the ecosystem well-being disturbance, is thus obtained.
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Affiliation(s)
- Victor Dyomin
- Laboratory for Radiophysical and Optical Methods of Environmental Research, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia; (V.D.); (N.K.); (I.P.)
| | - Alexandra Davydova
- Laboratory for Radiophysical and Optical Methods of Environmental Research, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia; (V.D.); (N.K.); (I.P.)
| | - Nikolay Kirillov
- Laboratory for Radiophysical and Optical Methods of Environmental Research, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia; (V.D.); (N.K.); (I.P.)
| | - Oksana Kondratova
- Center for Biotesting of Nanotechnologies and Nanomaterials Safety, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia; (O.K.); (Y.M.); (S.M.); (T.M.)
| | - Yuri Morgalev
- Center for Biotesting of Nanotechnologies and Nanomaterials Safety, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia; (O.K.); (Y.M.); (S.M.); (T.M.)
| | - Sergey Morgalev
- Center for Biotesting of Nanotechnologies and Nanomaterials Safety, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia; (O.K.); (Y.M.); (S.M.); (T.M.)
| | - Tamara Morgaleva
- Center for Biotesting of Nanotechnologies and Nanomaterials Safety, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia; (O.K.); (Y.M.); (S.M.); (T.M.)
| | - Igor Polovtsev
- Laboratory for Radiophysical and Optical Methods of Environmental Research, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia; (V.D.); (N.K.); (I.P.)
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Convolution Neural Network for the Prediction of Cochlodinium polykrikoides Bloom in the South Sea of Korea. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2021. [DOI: 10.3390/jmse10010031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this study, the occurrence of Cochlodinium polykrikoides bloom was predicted based on spatial information. The South Sea of Korea (SSK), where C. polykrikoides bloom occurs every year, was divided into three concentrated areas. For each domain, the optimal model configuration was determined by designing a verification experiment with 1–3 convolutional neural network (CNN) layers and 50–300 training times. Finally, we predicted the occurrence of C. polykrikoides bloom based on 3 CNN layers and 300 training times that showed the best results. The experimental results for the three areas showed that the average pixel accuracy was 96.22%, mean accuracy was 91.55%, mean IU was 81.5%, and frequency weighted IU was 84.57%, all of which showed above 80% prediction accuracy, indicating the achievement of appropriate performance. Our results show that the occurrence of C. polykrikoides bloom can be derived from atmosphere and ocean forecast information.
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Robinson KL, Sponaugle S, Luo JY, Gleiber MR, Cowen RK. Big or small, patchy all: Resolution of marine plankton patch structure at micro- to submesoscales for 36 taxa. SCIENCE ADVANCES 2021; 7:eabk2904. [PMID: 34797707 PMCID: PMC8604402 DOI: 10.1126/sciadv.abk2904] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 09/29/2021] [Indexed: 06/03/2023]
Abstract
Despite the ecological importance of microscale (0.01–1 meter) and fine-scale (1 to hundreds of meters) plankton patchiness, the dimensions and taxonomic identity of patches in the ocean are nearly unknown. We used underwater imaging to identify the position, horizontal length scale, and density of taxa-specific patches of 32 million organisms representing 36 taxa (200 micrometers to 20 centimeters) in the continental and oceanic environments of a subtropical, western boundary current. Patches were the most frequent in shallow, continental waters. For multiple taxa, patch count varied parabolically with background density. Taxa-specific patch length and organism size exhibited negative size scaling relationships. Organism size explained 21 to 30% of the variance in patch length. The dominant length scale was phylogenetically random and <100 meters for 64% of taxa. The predominance of micro- and fine-scale patches among a diverse suite of plankton suggests social and coactive processes may contribute to patch formation.
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Affiliation(s)
- Kelly L. Robinson
- Department of Biology, University of Louisiana at Lafayette, Lafayette, LA, USA
| | - Su Sponaugle
- Department of Integrative Biology, Oregon State University, Corvallis, OR, USA
| | - Jessica Y. Luo
- NOAA Geophysical Fluid Dynamics Laboratory, Princeton University Forrestal Campus, Princeton, NJ, USA
| | - Miram R. Gleiber
- Department of Integrative Biology, Oregon State University, Corvallis, OR, USA
| | - Robert K. Cowen
- Hatfield Marine Science Center, Oregon State University, Newport, OR, USA
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Wang J, Yang M, Ding Z, Zheng Q, Wang D, Kpalma K, Ren J. Detection of the Deep-Sea Plankton Community in Marine Ecosystem with Underwater Robotic Platform. SENSORS (BASEL, SWITZERLAND) 2021; 21:6720. [PMID: 34695933 PMCID: PMC8537131 DOI: 10.3390/s21206720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/24/2021] [Accepted: 10/05/2021] [Indexed: 11/29/2022]
Abstract
Variations in the quantity of plankton impact the entire marine ecosystem. It is of great significance to accurately assess the dynamic evolution of the plankton for monitoring the marine environment and global climate change. In this paper, a novel method is introduced for deep-sea plankton community detection in marine ecosystem using an underwater robotic platform. The videos were sampled at a distance of 1.5 m from the ocean floor, with a focal length of 1.5-2.5 m. The optical flow field is used to detect plankton community. We showed that for each of the moving plankton that do not overlap in space in two consecutive video frames, the time gradient of the spatial position of the plankton are opposite to each other in two consecutive optical flow fields. Further, the lateral and vertical gradients have the same value and orientation in two consecutive optical flow fields. Accordingly, moving plankton can be accurately detected under the complex dynamic background in the deep-sea environment. Experimental comparison with manual ground-truth fully validated the efficacy of the proposed methodology, which outperforms six state-of-the-art approaches.
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Affiliation(s)
- Jiaxing Wang
- School of Information Science and Engineering, Shandong University, Jinan 266237, China; (J.W.); (Q.Z.); (D.W.)
| | - Mingqiang Yang
- School of Information Science and Engineering, Shandong University, Jinan 266237, China; (J.W.); (Q.Z.); (D.W.)
- Shenzhen Research Institute, Shandong University, Shenzhen 518000, China
| | - Zhongjun Ding
- China National Deep Sea Center, Qingdao 266237, China
| | - Qinghe Zheng
- School of Information Science and Engineering, Shandong University, Jinan 266237, China; (J.W.); (Q.Z.); (D.W.)
| | - Deqiang Wang
- School of Information Science and Engineering, Shandong University, Jinan 266237, China; (J.W.); (Q.Z.); (D.W.)
| | | | - Jinchang Ren
- Centre for Excellence in Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK;
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Lieber L, Langrock R, Nimmo-Smith WAM. A bird's-eye view on turbulence: seabird foraging associations with evolving surface flow features. Proc Biol Sci 2021; 288:20210592. [PMID: 33906396 PMCID: PMC8079999 DOI: 10.1098/rspb.2021.0592] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 04/01/2021] [Indexed: 11/12/2022] Open
Abstract
Understanding physical mechanisms underlying seabird foraging is fundamental to predict responses to coastal change. For instance, turbulence in the water arising from natural or anthropogenic structures can affect foraging opportunities in tidal seas. Yet, identifying ecologically important localized turbulence features (e.g. upwellings approximately 10-100 m) is limited by observational scale, and this knowledge gap is magnified in volatile predators. Here, using a drone-based approach, we present the tracking of surface-foraging terns (143 trajectories belonging to three tern species) and dynamic turbulent surface flow features in synchrony. We thereby provide the earliest evidence that localized turbulence features can present physical foraging cues. Incorporating evolving vorticity and upwelling features within a hidden Markov model, we show that terns were more likely to actively forage as the strength of the underlying vorticity feature increased, while conspicuous upwellings ahead of the flight path presented a strong physical cue to stay in transit behaviour. This clearly encapsulates the importance of prevalent turbulence features as localized foraging cues. Our quantitative approach therefore offers the opportunity to unlock knowledge gaps in seabird sensory and foraging ecology on hitherto unobtainable scales. Finally, it lays the foundation to predict responses to coastal change to inform sustainable ocean development.
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Affiliation(s)
- Lilian Lieber
- School of Chemistry and Chemical Engineering, Queen's University Belfast, Marine Laboratory, 12–13 The Strand, Portaferry BT22 1PF, Northern Ireland, UK
| | - Roland Langrock
- Department of Business Administration and Economics, Bielefeld University, Postfach 10 01 31, 33501 Bielefeld, Germany
| | - W. Alex M. Nimmo-Smith
- School of Biological and Marine Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
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A Study on Enhancement of Fish Recognition Using Cumulative Mean of YOLO Network in Underwater Video Images. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2020. [DOI: 10.3390/jmse8110952] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the underwater environment, in order to preserve rare and endangered objects or to eliminate the exotic invasive species that can destroy the ecosystems, it is essential to classify objects and estimate their number. It is very difficult to classify objects and estimate their number. While YOLO shows excellent performance in object recognition, it recognizes objects by processing the images of each frame independently of each other. By accumulating the object classification results from the past frames to the current frame, we propose a method to accurately classify objects, and count their number in sequential video images. This has a high classification probability of 93.94% and 97.06% in the test videos of Bluegill and Largemouth bass, respectively. The proposed method shows very good classification performance in video images taken of the underwater environment.
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