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A highly sensitive underwater video system for use in turbid aquaculture ponds

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The turbid, low-light waters characteristic of aquaculture ponds have made it difficult or impossible for previous video cameras to provide clear imagery of the ponds’ benthic habitat. We developed a highly sensitive, underwater video system (UVS) for this particular application and tested it in shrimp ponds having turbidities typical of those in southern Taiwan. The system’s high-quality video stream and images, together with its camera capacity (up to nine cameras), permit in situ observations of shrimp feeding behavior, shrimp size and internal anatomy, and organic matter residues on pond sediments. The UVS can operate continuously and be focused remotely, a convenience to shrimp farmers. The observations possible with the UVS provide aquaculturists with information critical to provision of feed with minimal waste; determining whether the accumulation of organic-matter residues dictates exchange of pond water; and management decisions concerning shrimp health.
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Scientific RepoRts | 6:31810 | DOI: 10.1038/srep31810
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A highly sensitive underwater video
system for use in turbid aquaculture
ponds
Chin-Chang Hung1,2, Shih-Chieh Tsao1, Kuo-Hao Huang1, Jia-Pu Jang3, Hsu-Kuang Chang3 &
Fred C. Dobbs2
The turbid, low-light waters characteristic of aquaculture ponds have made it dicult or impossible for
previous video cameras to provide clear imagery of the ponds’ benthic habitat. We developed a highly
sensitive, underwater video system (UVS) for this particular application and tested it in shrimp ponds
having turbidities typical of those in southern Taiwan. The system’s high-quality video stream and
images, together with its camera capacity (up to nine cameras), permit in situ observations of shrimp
feeding behavior, shrimp size and internal anatomy, and organic matter residues on pond sediments.
The UVS can operate continuously and be focused remotely, a convenience to shrimp farmers. The
observations possible with the UVS provide aquaculturists with information critical to provision of
feed with minimal waste; determining whether the accumulation of organic-matter residues dictates
exchange of pond water; and management decisions concerning shrimp health.
Shrimp is one of the worlds most popular seafoods. Global production of farmed shrimp was approximately
3.5 million tonnes in 20131. Two of the most important issues in shrimp aquaculture are welfare (or health) and
pond management (feed and water quality). Shrimp welfare can be abetted in part by using specic pathogen free
(SPF) shrimp when stocking farms with postlarvae. Issues of pond management, however, are more dicult to
control during the normal growth period of shrimp (from postlarvae to marketable shrimp), 5 to 8 months or
longer. Shrimp growth is highly inuenced by the amount of food available, however, overfeeding not only wastes
feed, but the excess organic-matter residue results in poor water quality, increasing aquaculture costs. erefore,
it is crucial to monitor the provision of feed and feeding extent in farmed shrimp ponds.
Water transparency in shrimp ponds is largely aected by high concentrations of suspended particulate mat-
ter attributable to bioocs, phytoplankton, and residues of feed and shrimp waste2–4; visibility is oen less than
100 cm. It is a considerable challenge, therefore, to conduct an underwater visual inspection in such highly tur-
bid waters. Traditionally, checks of feeding trays have been used to implement feed control, but daily checking
of feed trays requires skilled labor; furthermore, feed tray analysis is subjective5. Acoustic control and imaging
sonar improve feeding productivity within sh cage and shrimp farms, and measurement of swimming patterns
and body length of cultured sh5–7, but the technologies cannot deliver real-time images of shrimp feeding and
welfare in the turbid benthic layer.
Underwater video systems have been used since the 1950s to monitor inter alia marine plankton, sh behavior,
and freshwater biodiversity8–11. For the most part, however, these video systems have been operated in clear-water
environments, including coastal sh cages. In this study, we developed and used a sensitive, underwater video
system (UVS) to monitor benthic conditions and shrimp feeding in the turbid, low-visibility waters of shrimp
ponds. e specic issues we addressed were to: (1) understand shrimp feeding, because excess feed incurs waste
and causes poor water quality; (2) examine the benthic surface for fecal pellets and excess organic matter, both of
which may result in increased pathogen concentrations and a fouled bottom; (3) visualize shrimp in turbid waters
to estimate their size and behavior, the latter because it can indicate water quality and infection by pathogens;
(4) provide in situ underwater images to sh farmers to reduce their economic loss during extremely cold weather.
e sensitive UVS we describe here has strong potential to be used in commercial shrimp and other aquaculture
operations.
1Department of Oceanography, and Asia-Pacific Ocean Research Center, National Sun Yat-Sen University,
Kaohsiung, 80424, Taiwan. 2Department of Ocean, Earth and Atmospheric Sciences, Old Dominion University,
Norfolk, VA, 23529 USA. 3Taiwan Ocean Research Institute, Kaohsiung, Taiwan. Correspondence and requests for
materials should be addressed to C.-C.H. (email: cchung@mail.nsysu.edu.tw)
Received: 03 March 2016
Accepted: 26 July 2016
Published: 24 August 2016
OPEN
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Results and Discussion
Monitoring shrimp feeding in turbid water. In most countries of Southeast Asia, including Taiwan and
China, shrimp farms usually grow animals at high densities (> 100 shrimps m2) in closed greenwater ponds
or biooc systems. e water used, therefore, oen is highly turbid due to suspended material, phytoplank-
ton, or both. Usually, visibility is approximately 100 cm, but sometimes less than 50 cm (Fig.1). Mean values of
total suspended material (TSM) in the three shrimp ponds (P1, P2, P3) on the NSYSU campus ranged between
4.4 and 48 mg/L, consistent with the range in lakes and commercial shrimp ponds across southern Taiwan
(3.8 to 50 mg/L, Fig.2). Turbidity in the UVS images presented here, therefore, is representative of conditions in
Taiwanese shrimp farms.
e low-light intensity characteristic of turbid water has proven problematic for camera systems in the absence
of auxiliary lighting8. e UVS, however, functions well even at 15 lux or less. Selected UVS images (snapshot
mode) serve to demonstrate the systems ability to visualize evidence of shrimp feeding, even in aquaculture
Figure 1. e underwater video system (UVS) used in shrimp ponds of dierent turbidities at the
campus of National Sun Yat-Sen University. Pond 1: low turbidity water (TSM = 4.4~6.4 mg/L, mean
value = 5.6 ± 0.9 mg/L, n = 4), Pond 2: low turbidity water (TSM = 5.2~8.5 mg/L, mean value = 6.8 ± 1.4 mg/L,
n = 4), Pond 3: high turbidity water (TSM = 27~73 mg/L, mean value = 48 ± 17 mg/L, n = 5). PoE = Power over
Ethernet. TSM: total suspended matter.
Figure 2. Concentrations of total suspended matter (TSM) in shrimp ponds from dierent locations.
P-1 to P-3 (within red rectangle) represent concentrations of TSM in shrimp ponds 1, 2, and 3 respectively.
Other TSM concentrations are from lakes (labeled L-A and L-B) or shrimp/sh farms (labeled P-A1 to P-G;
PA-1 and PA-2 represent duplicate sampling at pond A) in southern Taiwan.
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ponds. Figure3 contains images of shrimp ponds 1, 2, and 3 before (panels A,C,E) and aer (panels B,D,F) pro-
vision with formulated feed pellets, indicated by red circles. In low-turbidity ponds 1 and 2 (panels A-D), shrimp
are visible before and during feeding. Even in pond 3, characterized by high TSM and bioocs, feeding pellets are
visible on the sediment surface (panels E,F).
Traditionally, shrimp farmers determine the rate and extent of food consumption using a feeding tray to
which pellets are added. e tray is lowered into the pond, retrieved aer some tens of minutes, and inspected to
infer shrimp feeding. is method relies heavily on the farmers experience and can be misrepresentative in cases
of feeding lag or over feeding during the short time the tray is deployed. In contrast, clear, real-time images of
food pellets and shrimp feeding behavior provided by the UVS allow a temporally integrated ne-tuning of food
supply. Less well resolved images taken in highly turbid waters (e.g., Pond 3) also provide important information,
unavailable from feeding-tray assessments, permitting farmers to adjust the amount of feed accordingly. Overall,
therefore, the UVS largely improves on the traditional feeding-tray method to monitor shrimp feeding.
Acoustic control systems, used to improve feeding productivity in sh cages and shrimp farms5,6, are suitable
for clear-water systems or shrimp ponds of large size (e.g., > 10 ha). Acoustic control may not be cost eective in
Taiwan, ailand, or China, however, because most shrimp farms in those countries range in size from approx-
imately 0.16 to several ha2. In addition, a high percentage of intensive shrimp farms in Taiwan and ailand
are owned by small-scale operators2 who unlike their large-scale counterparts, do not require or cannot aord
pioneering instrumentation. Although the UVS is not without cost, it provides not only real-time feeding infor-
mation, but additional insights as well (next section).
Evaluating excess organic matter, shrimp size, and shrimp health. Jackson et al.3 reported that
sediments in shrimp ponds contain 14% (in terms of total nitrogen) of formulated food delivered to the shrimp.
is excess organic matter, plus that from shrimp feces, phytodetritus, etc., provides a substantial nutrient source
for bacteria, some of which may be pathogenic. To reduce bacterial load overall and keep water quality high in
ailand, shrimp ponds are heavily aerated to concentrate particulate organic waste at their center, rather than
Figure 3. Images showing formulated feed pellets (within red circles) upon the bottom of ponds having
dierent turbidities (panels A,C,E: before feeding at ponds 1, 2 and 3; panels B,D,F: during feeding at ponds
1, 2, and 3).
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have it distributed randomly across the pond bottom2. Conrming a concentrated distribution, however, has
previously not been easily accomplished. e UVS makes possible rapid detection of waste-rich organic sediment
in turbid waters or greenwater systems, for example, the thick layer of organic detritus and formulated feed pellets
seen in Fig.4A,B. With clear imagery of the benthic surface, shrimp farmers can decide when and to what degree
to clean pond sediments or exchange fouled pond water with fresh seawater.
e UVS also can be used to estimate shrimp size in situ, precluding the need to collect them with nets for
length measurements. For example, with reference to a ruler attached to a brick, we estimated two shrimp in
Fig.4B each to be approximately 10 cm long. In a calibration exercise, these estimates correlated strongly with
manual measurements of shrimp length: y = 1.04x-0.4, r = 0.99, p < 0.005, n = 20, where y is estimated size (cm)
and x = measured size (cm) (Fig.5). Deployment of a standard-sized mesh (e.g., from netting) on the sediment
surface would facilitate size estimates of multiple shrimp and account for changing perspective with distance from
the camera.
One can assess the health status of shrimp according to their swimming and feeding behaviors and body trans-
parency12. e resolution provided by the UVS (see video of healthy shrimp in Supplemental Material) makes
possible detailed assessment of behavior and inspection of internal organs. Abnormal behaviors typically caused
Figure 4. (A) A furrow in the surface layer of detritus (red oval) indicates the thick layer of organic matter
on the sediments in shrimp pond 1. (B) Two shrimp immediately in front of a white ruler (scale bar = 15 cm)
axed to a brick. (C) Image of a grouper in a turbid pond. (D) Image of multiple shrimp at night. At light
levels < 15 lux, black and white images, not color, are returned.
Figure 5. Relationship between estimated length (from video image) and measured length of shrimp.
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by poor water quality or disease, including lethargy, sluggish swimming, and spiral swimming, can be easily seen.
Given its ability to monitor shrimp feeding, behavior, and internal organs, even in turbid waters, the UVS can
serve farmers as an early warning system to detect unhealthy shrimp, making possible an immediate response to
overfeeding, disease, or poor water quality.
Providing real-time underwater images to aquaculture farmers. Asia is the biggest aquaculture
production area in the world and 70% of global aquaculture products are produced by China and Taiwan13,14. In
January 2016, a lethal cold snap in those two regions killed aquaculture stock including sh, clams, and shrimp15.
In Taiwan alone, losses were estimated at more than $100M US16. Fish farmers were able to promptly harvest
some types of cold-stunned sh, e.g., milksh and tilapia, and reduced their economic loss. Losses of demersal
sh and shrimp were profound, however, in large part because they could not be seen in high-turbidity water. In
particular, 2- to 3-year old groupers weighing 10 to 30 kg were killed but unseen until they oated to the surface
of sh ponds several days aer the extremely cold weather. ey were spoiled and unharvestable. In cases such as
these rare cold snaps (ca. once per decade), the UVS could supply sh farmers with images pertinent to making
harvest decisions to save their stocks or at least to reduce their property loss (Fig.4C).
Advantages of the UVS and its future improvements. Struthers et al.17 recently reviewed commercial,
portable, underwater cameras that have been used under relatively clear conditions. Compared to the UVS, such
cameras have limitations: (1) ey are battery powered (battery life time 2~3 hours), whereas the UVS uses a PoE
cable; no battery re-charge is needed. (2) Most commercial cameras cannot provide real-time images; the UVS
can provide them from several cameras (up to 9) simultaneously. (3) Each camera in the UVS can be operated
manually or controlled by computer or smart phone via internet and images can be stored for 5 days (up to a
10-day capacity). (4) Functions of the UVS can be expanded to include sensors for temperature, salinity, pH, dis-
solved oxygen, etc. (5) e UVS can be operated in sh ponds long-term (24 hours/day for more than 1 year) and
the depth of deployment can reach 1000 m if needed (a dierent housing is used). (6) e resolution of the UVS
can be upgraded if budget is not a consideration.
Is there an eect on shrimp behavior at very low light levels (< 15 lux) when the IR bulbs are activated? Some
sh are sensitive to IR light in highly turbid habitats18, but shrimp likely are not, based on reports of their spectral
sensitivity. Vision in the penaeid shrimp, Sergestes similis, has a single peak of sensitivity from 480 to 520 nm19
(Lindsay et al.19). Frank and Widder20 tested vision in 12 species of mesopelagic crustaceans, including shrimp,
and found all had a strong peak within the range 470 to 500 nm. In investigating the eects of dierent light
sources on growth of Litopenaeus vannamei, You et al.21 showed the shrimp grew rapidly when illuminated by a
metal halide lamp (MHL). e spectrum of a typical MHL, however, is between 385 nm and 674 nm, suggesting
only visible light aects shrimp growth. Finally, Sanudin et al.22 concluded that lighting conditions, including IR
wavelengths, did not aect feeding by post larvae (PL10 and older) of Penaeus vannamei. erefore, although we
did not run IR sensitivity experiments, the literature suggests shrimp are insensitive to IR light.
Judicious siting of the video cameras, which currently “see” only a xed area, can allow assessment of the pond
overall. To increase coverage for large shrimp ponds, farmers could install additional cameras; one server can
control nine cameras based on current technology. In the future, we envision rotating UVS, even mobile ones, to
increase the area monitored. Such systems could be connected with soware to monitor and control the ponds’
environment based on real-time imagery. at is, the UVS is a rst step in developing fully automated systems to
detect the amount of food available to the shrimp, provide food when needed, assess the degree of accumulation
of sediment in the ponds, and even dierentiate between low light levels caused by turbidity vs absence of sun-
light. We understand conceptually how machine learning, integrated with the UVS, can achieve these goals and
hope in the future to investigate them.
Materials and Methods
Underwater video system (UVS). e underwater video system (UVS) contains a 500-GB hard drive
(expandable up to 2-TB), a server, and a power supply (each camera operates at 12 V and 500 mA) supporting
three custom manufactured underwater cameras; the system can be congured with as many as nine cameras
(Fig.1). Each camera is emplaced autonomously. e real-time image from each is processed by a built-in digital
signal processor (DSP) and sent to a server through a power over Ethernet (PoE) cable, then transported to an
internet account via a wireless network.
Each camera has two 3-watt infrared (780 nm) LED bulbs, contained within a waterproof, aluminum alloy
(Aluminum 6061-T6) housing with an acrylic window. Cameras were xed approximately 20~30 cm above the
bottom of shrimp ponds to monitor shrimp activity, feeding status, welfare, and benthic sediments. e UVS cap-
tures color images when the light intensity is higher than 15 lux; below that level, the infrared LED bulbs are con-
trolled by an IR-Cut sensor and turn on automatically, generating black and white images. e system is reliable in
seawater shrimp ponds (salinity ranging from 30 to 34); the UVS operated at depths of 1.2 to 1.8 m for more than
12 months. e acrylic window needs to be cleaned of biofouling every 3 to 4 days. On sunny and most cloudy
days, the UVS operates in color mode because the light intensity at the bottom of the ponds is approximately
790 to 1,580 lux (about 1% of surface light intensity, based on annual sunlight intensity data in southern Taiwan
(www.cwb.gov.tw). erefore, the UVS can be used in low transparency waters during the daytime without extra
light sources. e underwater images can be accessed remotely by a smart phone or through the internet.
Video camera specifications. The camera (OV2710) is a true full HP (1080p) CMOS image sensor
designed specically to deliver high-end HD video to a digital video camcorder with a display resolution of
1920 × 1080 pixels, operating at 30 frames per sec (for more details, see the OV2710 commercial website, OV2710
product brief). Built with Omni Visions proprietary 3 μ m OmniPixeI3-HS high sensitivity pixel technology, the
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OV2710 delivers low light sensitivity of 3300 mV/lux-sec, S/N ratio of 39 dB, dark current of 10 mV/sec, and a
peak dynamic range of 69 dB, which in combination allow the camera to operate in conditions ranging from
strong light to nearly complete darkness (below 15 lux), such as in highly turbid aquaculture ponds, even at night,
so that 24 hour surveillance of shrimp or sh is possible (Fig.4D).
Measurement of total suspended matter (TSM). Concentrations of TSM in lakes and aquaculture
ponds in southern Taiwan were measured using the method of Hung et al.23. Briey, 20 to 150 ml of water was
ltered through a tared 25 mm polycarbonate lter (pore size = 0.4 μ m), then the lter was gently rinsed with
15 to 20 mL of Milli-Q water to remove salts. e lter was dried, weighed on a micro balance, and the mass of
suspended matter was calculated by dierence.
Application of UVS in aquaculture ponds of dierent turbidity. We conducted observations in three
shrimp ponds on the campus of National Sun Yat-Sen University from June 2014 to November 2015. Each pond
contained between 40 to 70 tonnes of seawater and supported at a minimum several thousand white shrimp,
Penaeus vannamei. Shrimp feed pellets were evenly dispensed three times daily. TSM concentrations in ponds
1, 2, and 3 ranged from 4.6 to 6.4, 5.2 to 8.2, and 27 to 73 mg/L, respectively (Fig.2).
We observed shrimp behavior and feeding via real-time UVS. e UVS image stream was recorded, allowing
review of previous activities (see video in Supplemental Materials). We evaluated benthic surface conditions with
respect to feed pellets, fecal pellets, and excess organic matter. We estimated the length of shrimp that swam or
crawled adjacent to a ruler (15 cm long) axed to a brick. We calibrated our length estimates using shrimp indi-
vidually measured by hand, placed in a large tank lled with shrimp pond water, and measured again using the
UVS and a ruler.
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Acknowledgements
We would like to express our sincere thanks to Y.Y. Shih, C.J. Chen, C. Chen and S.Y. Huang for maintaining the
shrimp. is research (#104-3113-M-110-002 and #104-2611-M-110-020) was supported by MOST of Taiwan.
We also thank positive comments from two anonymous reviewers.
Author Contributions
C.-C.H. conceived the idea and wrote the manuscript with F.C.D., S.-C.T. and K.-H.H. did the experiments and
prepared the images and gures. J.-P.J. and H.-K.C. developed the UVS. All authors reviewed the manuscript.
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Scientific RepoRts | 6:31810 | DOI: 10.1038/srep31810
Additional Information
Supplementary information accompanies this paper at http://www.nature.com/srep
Competing nancial interests: e authors declare no competing nancial interests.
How to cite this article: Hung, C.-C. et al. A highly sensitive underwater video system for use in turbid
aquaculture ponds. Sci. Rep. 6, 31810; doi: 10.1038/srep31810 (2016).
is work is licensed under a Creative Commons Attribution 4.0 International License. e images
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unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license,
users will need to obtain permission from the license holder to reproduce the material. To view a copy of this
license, visit http://creativecommons.org/licenses/by/4.0/
© e Author(s) 2016
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... Artificial Intelligence (AI) has emerged as a pivotal tool in enhancing our understanding of the feeding behaviour of L. vannamei, a crucial species in aquaculture [19]. Through the utilization of AI-driven techniques, researchers have been able to unravel complex patterns and relationships within the intricate dynamics of shrimp feeding [20]. AI facilitates the processing of vast amounts of behavioural data, enabling the identification of nuanced responses to various stimuli, such as feed availability, environmental conditions, and individual characteristics [21]. ...
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"I want to dedicate this book to my parents, who have always supported my career objectives and actively tried to provide me with the protected academic time I needed to achieve those goals. This book is focused on current data science research issues that provide practical solutions to the industry quickly. Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. I would like to start by expressing my gratitude to the Management for their invaluable support and inspiration. I want to express my gratitude to the Co-Editor, Mr. Prateek Gupta, for their invaluable reviews that helped improve the chapter’s quality, coherence, and content presentation. I greatly appreciate their effort to review the submitted chapters with great competence, kindness, and accuracy. I would also like to express my appreciation to the authors for their invaluable support and contribution. Without IIP Proceedings' backing, this book would not have been published. IIP Proceedings has my sincere gratitude for accepting my proposal and believing in me throughout the publication process. Finally, if this book is a success, I will dedicate it to my students and colleagues, past and present. The love and support of my parents and family have been a constant source of motivation and inspiration for me. Without their backing, this book would not have been possible. I would like to express my thanks for their support."
... 2023). Likewise, innovative approaches to managing feed application and organic waste in fishponds, known as smart aquaculture, have been introduced to enhance aquaculture production while minimizing environmental impacts (Hung et al., 2016;Chiu et al., 2022;Zhang et al., 2023). We suggest applying a box model that includes factors such as feeding, aeration, water temperature variations, input water quality, and other boundary conditions (wind speed, daytime length, water depth, and benthic materials) to estimate the CO 2 emissions. ...
... However, it is foreseeable that technologies for monitoring the welfare of farmed shrimp and those specifically targeting GAP will increasingly converge from now on. This development will typically take place through so-called "precision aquaculture" [256], which will include technologies such as the use of biosensors, data loggers, and early warning systems [21]; computer vision for animal monitoring, sensor networks (wireless and long range), robotics, and decision support tools, such as algorithms, the Internet of Things, and decision support systems [261][262][263]; as well as the use of high-quality and sensitive cameras for automatic detection and analysis of shrimp behaviour even in turbid waters [145,264]. Such technologies will, in turn, provide shrimp farmers with important information to manage feed supply with minimum waste and maximum feed efficiency; assess organic waste accumulation in ponds to optimise the water quality available to shrimp; improve management decisions related to animal health; and increasingly use animal behavioural signals as indicators of their welfare and the efficiency of management practices applied. ...
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