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Automatic Monitoring of Relevant Behaviors for Crustacean Production in Aquaculture: A Review. Animals (Basel) 2021; 11:ani11092709. [PMID: 34573675 PMCID: PMC8466386 DOI: 10.3390/ani11092709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/12/2021] [Accepted: 09/13/2021] [Indexed: 11/23/2022] Open
Abstract
Simple Summary Automatic behavior monitoring, also called automated analytics or automated reporting, is the ability of an analytics platform to auto-detect relevant insights—anomalies, trends, patterns—and deliver them to users in real time, without users having to manually explore their data to find the answers they need. An analytics platform with automated behavior monitoring uses algorithms to auto-analyze datasets to search for notable changes in data. It then generates alerts at fixed intervals or triggers (thresholds), and delivers the findings to each user, ready-made. In-aquaculture scoring of behavioral indicators of aquatic animal welfare is challenging, but the increasing availability of low-cost technology now makes the automated monitoring of behavior feasible. Abstract Crustacean farming is a fast-growing sector and has contributed to improving incomes. Many studies have focused on how to improve crustacean production. Information about crustacean behavior is important in this respect. Manual methods of detecting crustacean behavior are usually infectible, time-consuming, and imprecise. Therefore, automatic growth situation monitoring according to changes in behavior has gained more attention, including acoustic technology, machine vision, and sensors. This article reviews the development of these automatic behavior monitoring methods over the past three decades and summarizes their domains of application, as well as their advantages and disadvantages. Furthermore, the challenges of individual sensitivity and aquaculture environment for future research on the behavior of crustaceans are also highlighted. Studies show that feeding behavior, movement rhythms, and reproduction behavior are the three most important behaviors of crustaceans, and the applications of information technology such as advanced machine vision technology have great significance to accelerate the development of new means and techniques for more effective automatic monitoring. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Our purpose is to provide researchers and practitioners with a better understanding of the state of the art of automatic monitoring of crustacean behaviors, pursuant of supporting the implementation of smart crustacean farming applications.
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The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms. SENSORS 2020; 20:s20061751. [PMID: 32245204 PMCID: PMC7146366 DOI: 10.3390/s20061751] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 03/13/2020] [Accepted: 03/19/2020] [Indexed: 02/04/2023]
Abstract
Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.
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A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring. SENSORS 2020; 20:s20061614. [PMID: 32183233 PMCID: PMC7146179 DOI: 10.3390/s20061614] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 03/08/2020] [Accepted: 03/10/2020] [Indexed: 11/17/2022]
Abstract
This paper presents the technological developments and the policy contexts for the project “Autonomous Robotic Sea-Floor Infrastructure for Bentho-Pelagic Monitoring” (ARIM). The development is based on the national experience with robotic component technologies that are combined and merged into a new product for autonomous and integrated ecological deep-sea monitoring. Traditional monitoring is often vessel-based and thus resource demanding. It is economically unviable to fulfill the current policy for ecosystem monitoring with traditional approaches. Thus, this project developed platforms for bentho-pelagic monitoring using an arrangement of crawler and stationary platforms at the Lofoten-Vesterålen (LoVe) observatory network (Norway). Visual and acoustic imaging along with standard oceanographic sensors have been combined to support advanced and continuous spatial-temporal monitoring near cold water coral mounds. Just as important is the automatic processing techniques under development that have been implemented to allow species (or categories of species) quantification (i.e., tracking and classification). At the same time, real-time outboard processed three-dimensional (3D) laser scanning has been implemented to increase mission autonomy capability, delivering quantifiable information on habitat features (i.e., for seascape approaches). The first version of platform autonomy has already been tested under controlled conditions with a tethered crawler exploring the vicinity of a cabled stationary instrumented garage. Our vision is that elimination of the tether in combination with inductive battery recharge trough fuel cell technology will facilitate self-sustained long-term autonomous operations over large areas, serving not only the needs of science, but also sub-sea industries like subsea oil and gas, and mining.
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Aguzzi J, Chatzievangelou D, Marini S, Fanelli E, Danovaro R, Flögel S, Lebris N, Juanes F, De Leo FC, Del Rio J, Thomsen L, Costa C, Riccobene G, Tamburini C, Lefevre D, Gojak C, Poulain PM, Favali P, Griffa A, Purser A, Cline D, Edgington D, Navarro J, Stefanni S, D'Hondt S, Priede IG, Rountree R, Company JB. New High-Tech Flexible Networks for the Monitoring of Deep-Sea Ecosystems. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:6616-6631. [PMID: 31074981 DOI: 10.1021/acs.est.9b00409] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Increasing interest in the acquisition of biotic and abiotic resources from within the deep sea (e.g., fisheries, oil-gas extraction, and mining) urgently imposes the development of novel monitoring technologies, beyond the traditional vessel-assisted, time-consuming, high-cost sampling surveys. The implementation of permanent networks of seabed and water-column-cabled (fixed) and docked mobile platforms is presently enforced, to cooperatively measure biological features and environmental (physicochemical) parameters. Video and acoustic (i.e., optoacoustic) imaging are becoming central approaches for studying benthic fauna (e.g., quantifying species presence, behavior, and trophic interactions) in a remote, continuous, and prolonged fashion. Imaging is also being complemented by in situ environmental-DNA sequencing technologies, allowing the traceability of a wide range of organisms (including prokaryotes) beyond the reach of optoacoustic tools. Here, we describe the different fixed and mobile platforms of those benthic and pelagic monitoring networks, proposing at the same time an innovative roadmap for the automated computing of hierarchical ecological information on deep-sea ecosystems (i.e., from single species' abundance and life traits to community composition, and overall biodiversity).
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Affiliation(s)
- Jacopo Aguzzi
- Instituto de Ciencias del Mar (ICM-CSIC) , Paseo Marítimo de la Barceloneta, 37-49 , 08012 Barcelona , Spain
| | | | - Simone Marini
- Institute of Marine Sciences , National Research Council of Italy (CNR) , 19036 La Spezia , Italy
| | - Emanuela Fanelli
- Department of Life and Environmental Sciences , Polytechnic University of Marche , 60121 Ancona , Italy
| | - Roberto Danovaro
- Department of Life and Environmental Sciences , Polytechnic University of Marche , 60121 Ancona , Italy
- Stazione Zoologica Anton Dohrn (SZN) , 80121 Naples , Italy
| | | | - Nadine Lebris
- Oceanological Observatory , CNRS LECOB, Sorbonne University , 66650 Banyuls-sur-mer , France
| | - Francis Juanes
- Department of Biology , University of Victoria , Victoria , British Columbia V8W 2Y2 , Canada
| | - Fabio C De Leo
- Department of Biology , University of Victoria , Victoria , British Columbia V8W 2Y2 , Canada
- Ocean Networks Canada (ONC) , University of Victoria , Victoria , British Columbia V8N 1V8 , Canada
| | - Joaquin Del Rio
- OBSEA, SARTI , Universitat Politècnica de Catalunya (UPC) , 08800 Barcelona , Spain
| | | | - Corrado Costa
- Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA-IT) , 00198 Monterotondo , Italy
| | - Giorgio Riccobene
- Istituto Nazionale di Fisica Nucleare (INFN), Laboratori Nazionali del Sud , 95125 Catania , Italy
| | - Cristian Tamburini
- Institut Méditerranéen d'Océanoloie (MIO) , 13288 Cedex 09 Marseille , France
| | - Dominique Lefevre
- Institut Méditerranéen d'Océanoloie (MIO) , 13288 Cedex 09 Marseille , France
| | - Carl Gojak
- DT INSU , 83507 La Seyne-sur-Mer , France
| | - Pierre-Marie Poulain
- Istituto Nazionale di Oceanografia e Geofisica Sperimentale (OGS) , 34010 Trieste , Italy
| | - Paolo Favali
- Istituto Nazionale di Geofisica e Vulcanologia (INGV) , 00143 Rome , Italy
- European Multidisciplinary Seafloor and Water-Column Observatory European Research Infrastructure Consortium (EMSO ERIC) , 00143 Rome , Italy
| | - Annalisa Griffa
- Institute of Marine Sciences , National Research Council of Italy (CNR) , 19036 La Spezia , Italy
| | - Autun Purser
- Alfred Wegener Institute (AWI) . 27515 Bremerhaven , Germany
| | - Danelle Cline
- Monterey Bay Aquarium Research Institute (MBARI) , Moss Landing , California 95039 , United States
| | - Duane Edgington
- Monterey Bay Aquarium Research Institute (MBARI) , Moss Landing , California 95039 , United States
| | - Joan Navarro
- Instituto de Ciencias del Mar (ICM-CSIC) , Paseo Marítimo de la Barceloneta, 37-49 , 08012 Barcelona , Spain
| | | | - Steve D'Hondt
- Graduate School of Oceanography , University of Rhode Island , Narragansett , Rhode Island 02882 , United States
| | - Imants G Priede
- University of Aberdeen , Aberdeen AB24 3FX , United Kingdom
- Hellenic Centre for Marine Research , 71003 Heraklion Crete , Greece
| | - Rodney Rountree
- Department of Biology , University of Victoria , Victoria , British Columbia V8W 2Y2 , Canada
- The Fish Listener , 23 Joshua Lane , Waquoit , Massachusetts 02536 , United States
| | - Joan B Company
- Instituto de Ciencias del Mar (ICM-CSIC) , Paseo Marítimo de la Barceloneta, 37-49 , 08012 Barcelona , Spain
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Matabos M, Hoeberechts M, Doya C, Aguzzi J, Nephin J, Reimchen TE, Leaver S, Marx RM, Branzan Albu A, Fier R, Fernandez‐Arcaya U, Juniper SK. Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? Methods Ecol Evol 2017. [DOI: 10.1111/2041-210x.12746] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Marjolaine Matabos
- Ifremer, Centre de Bretagne, REM/EEP, Laboratoire Environnement Profond 29280 Plouzané France
| | - Maia Hoeberechts
- Ocean Networks Canada University of Victoria Victoria BC V8W 2Y2 Canada
- Department of Computer Science University of Victoria Victoria BC Canada
| | - Carol Doya
- Institute of Marine Sciences (ICM‐CSIC) Paseo Marítimo de la Barceloneta 37‐49 Barcelona 08003 Spain
| | - Jacopo Aguzzi
- Institute of Marine Sciences (ICM‐CSIC) Paseo Marítimo de la Barceloneta 37‐49 Barcelona 08003 Spain
| | - Jessica Nephin
- Ocean Networks Canada University of Victoria Victoria BC V8W 2Y2 Canada
| | | | - Steve Leaver
- Department of Biology University of Victoria Victoria BC Canada
| | | | - Alexandra Branzan Albu
- Department of Computer Science University of Victoria Victoria BC Canada
- Department of Electrical and Computer Engineering University of Victoria Victoria BC Canada
| | - Ryan Fier
- Department of Electrical and Computer Engineering University of Victoria Victoria BC Canada
| | - Ulla Fernandez‐Arcaya
- Institute of Marine Sciences (ICM‐CSIC) Paseo Marítimo de la Barceloneta 37‐49 Barcelona 08003 Spain
| | - S. Kim Juniper
- Ocean Networks Canada University of Victoria Victoria BC V8W 2Y2 Canada
- School of Earth and Ocean Sciences and Biology Department University of Victoria Victoria BC Canada
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Strong JA, Elliott M. The value of remote sensing techniques in supporting effective extrapolation across multiple marine spatial scales. MARINE POLLUTION BULLETIN 2017; 116:405-419. [PMID: 28118970 DOI: 10.1016/j.marpolbul.2017.01.028] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 12/12/2016] [Accepted: 01/14/2017] [Indexed: 06/06/2023]
Abstract
The reporting of ecological phenomena and environmental status routinely required point observations, collected with traditional sampling approaches to be extrapolated to larger reporting scales. This process encompasses difficulties that can quickly entrain significant errors. Remote sensing techniques offer insights and exceptional spatial coverage for observing the marine environment. This review provides guidance on (i) the structures and discontinuities inherent within the extrapolative process, (ii) how to extrapolate effectively across multiple spatial scales, and (iii) remote sensing techniques and data sets that can facilitate this process. This evaluation illustrates that remote sensing techniques are a critical component in extrapolation and likely to underpin the production of high-quality assessments of ecological phenomena and the regional reporting of environmental status. Ultimately, is it hoped that this guidance will aid the production of robust and consistent extrapolations that also make full use of the techniques and data sets that expedite this process.
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Affiliation(s)
- James Asa Strong
- The Institute of Estuarine and Coastal Studies, University of Hull, Hull HU6 7RX, UK.
| | - Michael Elliott
- The Institute of Estuarine and Coastal Studies, University of Hull, Hull HU6 7RX, UK
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Su TC, Yang MD. Application of morphological segmentation to leaking defect detection in sewer pipelines. SENSORS 2014; 14:8686-704. [PMID: 24841247 PMCID: PMC4063020 DOI: 10.3390/s140508686] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2014] [Revised: 04/18/2014] [Accepted: 05/12/2014] [Indexed: 11/16/2022]
Abstract
As one of major underground pipelines, sewerage is an important infrastructure in any modern city. The most common problem occurring in sewerage is leaking, whose position and failure level is typically identified through closed circuit television (CCTV) inspection in order to facilitate rehabilitation process. This paper proposes a novel method of computer vision, morphological segmentation based on edge detection (MSED), to assist inspectors in detecting pipeline defects in CCTV inspection images. In addition to MSED, other mathematical morphology-based image segmentation methods, including opening top-hat operation (OTHO) and closing bottom-hat operation (CBHO), were also applied to the defect detection in vitrified clay sewer pipelines. The CCTV inspection images of the sewer system in the 9th district, Taichung City, Taiwan were selected as the experimental materials. The segmentation results demonstrate that MSED and OTHO are useful for the detection of cracks and open joints, respectively, which are the typical leakage defects found in sewer pipelines.
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Affiliation(s)
- Tung-Ching Su
- Department of Civil Engineering and Engineering Management, National Quemoy University, Da Xue Rd. 1, Kinmen 892, Taiwan.
| | - Ming-Der Yang
- Department of Civil Engineering, National Chung Hsing University, Taichung 402, Taiwan.
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A new colorimetrically-calibrated automated video-imaging protocol for day-night fish counting at the OBSEA coastal cabled observatory. SENSORS 2013; 13:14740-53. [PMID: 24177726 PMCID: PMC3871094 DOI: 10.3390/s131114740] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Revised: 10/22/2013] [Accepted: 10/22/2013] [Indexed: 11/16/2022]
Abstract
Field measurements of the swimming activity rhythms of fishes are scant due to the difficulty of counting individuals at a high frequency over a long period of time. Cabled observatory video monitoring allows such a sampling at a high frequency over unlimited periods of time. Unfortunately, automation for the extraction of biological information (i.e., animals' visual counts per unit of time) is still a major bottleneck. In this study, we describe a new automated video-imaging protocol for the 24-h continuous counting of fishes in colorimetrically calibrated time-lapse photographic outputs, taken by a shallow water (20 m depth) cabled video-platform, the OBSEA. The spectral reflectance value for each patch was measured between 400 to 700 nm and then converted into standard RGB, used as a reference for all subsequent calibrations. All the images were acquired within a standardized Region Of Interest (ROI), represented by a 2 × 2 m methacrylate panel, endowed with a 9-colour calibration chart, and calibrated using the recently implemented "3D Thin-Plate Spline" warping approach in order to numerically define color by its coordinates in n-dimensional space. That operation was repeated on a subset of images, 500 images as a training set, manually selected since acquired under optimum visibility conditions. All images plus those for the training set were ordered together through Principal Component Analysis allowing the selection of 614 images (67.6%) out of 908 as a total corresponding to 18 days (at 30 min frequency). The Roberts operator (used in image processing and computer vision for edge detection) was used to highlights regions of high spatial colour gradient corresponding to fishes' bodies. Time series in manual and visual counts were compared together for efficiency evaluation. Periodogram and waveform analysis outputs provided very similar results, although quantified parameters in relation to the strength of respective rhythms were different. Results indicate that automation efficiency is limited by optimum visibility conditions. Data sets from manual counting present the larger day-night fluctuations in comparison to those derived from automation. This comparison indicates that the automation protocol subestimate fish numbers but it is anyway suitable for the study of community activity rhythms.
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