SUGISAKAMASANORI

Journal of Robotics, Networking and Artificial Life
Vol. 9(2); September (2022), pp. 143–153
ISSN (Online):2352-6386; ISSN (Print):2405-9021
https://alife-robotics.org/jrnal.html

Research Article

Digital Transformation of Feeding Control Knowledge in Marine
Aquaculture using Current Sensors
Dominic B. Solpico, Yuya Nishida, Kazuo Ishii

Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan

ARTIC LE INFO
Article History
Received 08 December 2020
Accepted 04 July 2022

Keywords
DX
Aquaculture
Feeding
Underwater currents
Current sensors

ABSTRACT

Improving the efficiency of fish feeding contributes to achieving sustainable expansion of theaquaculture industry. However, expert knowledge on feeding remains reliant on experience. This
paper presents a new approach of applying digital transformation (DX) on such knowledge bymeasuring underwater currents induced by fishes as indicator of their hunger. A prototype sensor
suite consisting of current sensors, cameras, and an inertial measurement unit (IMU) wasconstructed to measure the currents around the fish cage, measure the motion of the suite, andrecord fish activity, particularly during feeding. An initial experiment was performed in two fish
cages during feeding activity. Current measurements were collected and analyzed together withthe recorded videos to relate the changes in current to the feeding activities. Results suggest thatthe currents increase around when feeding was started continuously and decline to zero aroundwhen it was stopped. However, more data needs to be collected and analyzed for a better
understanding of the relationship between the changes in current and fish activities so it can beused to optimize the feeding decisions of fish farmers.

© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

86.7% [5]. Poorly timed and excessive feeding leads topoor cost-efficiency in raising fish [6]. It was estimatedthat 8.26% of the supplied feed gets lost to theenvironment [7]. Costs expended on the uneaten feeds
would not be translated into further growth of the fishes
and subsequently into the increase in their value,
therefore reducing the potential income that could be
made. In addition, these feeds would sink to the bottomof the sea to decompose, releasing ammonia andphosphate, nutrients that accelerate the growth of harmful
algal blooms in the surrounding environment [8].
Decomposition of both feeds and algal blooms would
consume dissolved oxygen, depleting the supply for thefishes as a result. Such threat would lead to slower
growth rates, poorer quality of harvest, or at worst,
massive fish kills [8,9,10]. Not only the profitability of
the farm operation would be limited, but fish farmers
could even lose their investment as a result [3,6,8].

1. Introduction
While seafood consumption has been increasing every
year, capture fisheries production, facing issues ofoverfishing, has remained static with slight fluctuationsin the past decades. Since early 1990s, the aquacultureindustry has been expanding consistently to meet therising demand worldwide, making up 46% of the global
production in 2018 [1].
The problem arises in the industry’s sustainability.
Unsustainable management of fish farms not only posesa threat to their surrounding aquatic environment but also
to the health of the fish stocks [2,3,4]. One crucial issue
is the need for efficient decision making in fish feeding.
In many aquaculture operations, feeds make up a large
portion of the production costs, accounting for as high as

Corresponding author’s E-mail: solpico.dominic-bautista806@mail.kyutech.jp www.lsse.kyutech.ac.jp

143vision (CV), acoustic technologies, and sensors-based
technologies.
Recognizing fish feeding with computer vision has been
widely used in aquaculture in recent years, as opticalsensors and machine vision systems are becoming more
power and sensitive, while becoming less expensive
throughout the years [20]. However, a significant issue inthe application of computer vision is the complexity of
the aquaculture environments causing various
degradation to images [21]. While some methods use
near infrared to adapt to low lighting and turbid water,
their resolution and accuracy need to be improved.
Acoustic technologies overcome the problems ofdegradation brought by varying illumination and water
turbidity conditions affecting the performance ofcomputer vision, as sound propagates in water withminimal attenuation. However, each kind of method also
encounter specific problems. The use of passive acoustics
to detect feeding sounds are affected by ambient noisefrom aquaculture equipment and weather [22]. While
tracking fish position and movement using with clarityand precision even in marine environments, equipment isrelatively expensive [18,23].
Various sensor-based technologies are used for monitor
fish feeding. With biotelemetry, fishes are accurately
tracked with inertial measurement unit (IMU) or depth
sensors, with data transmitted acoustically [24]. However,this method is invasive, which may affect the welfare of
the fish. Implanting trackers would also be difficult to do
for thousands of fishes in a cage. The same problems
apply to using electromyogram (EMG) transmitters,
which classifies fish hunger states with high success [25].
On the other hand, measuring water quality using sensors
for parameters such as dissolved oxygen and temperature
are non-invasive [6,26]. Generating feeding decisions
using these sensors had high success rates, as thesemeasurements are related to fish hunger. However, these
are also susceptible to other water quality parameters [18].
A measurement that has been barely explored formaking fish feeding decisions is of underwater currents
generated by fish movements. It has been studied thatcircular swimming patterns of fish schools such as
salmon push water out of cages, inducing outward flow
[27,28,29]. It is also known that farmed fishes tend to
swim close to the surface when hungry, and swim at the
deeper parts of the cage when satiated, as shown in Fig.
1. By knowing the currents at different depths, we could
gain better understanding of the behavior of fishes in a

Japan, where marine aquaculture comprises majority of
the country's aquaculture production, is not exemptedfrom these issues. Production volume has remained
stagnant throughout the years, without significant
increases. With increasing prices of feed and imported
fish meal among other factors, income on fishing inmarine aquaculture households have decreased in 2018,as of the latest report [11].
The practice of feeding in Japanese fish farms involves
the use of feeding machines mounted on boats. Efficientfeeding has usually been achieved with decisions madeby expert farmers, who would start and stop the feeding
operation upon their sound assessment of the fishes’
behavior. This would reduce the feed conversion rate,
improving fish welfare and reducing costs [12]. In
addition, there is a significant difference in the quality of
the harvested fish fed based on expert and non-expert
decisions. Such decision making remains to be an “art,”
where prediction is still intuitive, subject to the expert’s
experience, and unquantifiable by a unified standard[13,14].
Digital transformation (DX), defined as the adoption of
disruptive digital technologies to increase productivity,
value creation and social welfare, has been embraced byan increasing number of industries, governments, andorganizations around the world with the emergence ofIndustry 4.0 [15]. It has been seen as enabler ofpredictability, efficiency, and productivity by reducing
operation costs and personnel, as well as increasing
feeding efficiency [16,17]. DX of experts’ knowledge infeeding would help them improve their feeding decisions,but also those of less experienced farmers. This would
not only improve the amount and quality of their harvestand consequently increase their income, but will also
increase the supply of high-quality seafood, lower theirprices, and at the same time minimize pollution in thefarm environment.
Especially with the recent advancements in artificial
intelligence, intelligent feeding control has become oneof main topics of current research efforts in aquaculture.
Various methods have been developed to recognize andanalyze fish feeding in water [12,18]. Applications varyfrom tracking fish movement to detecting feeds in water.
Some of these methods feed the information collected
into machine learning models to generate feeding
decisions [6,14,19]. Different information technologies
developed fall into three general categories: computer144

2. Sensor System Architecture
In the envisioned system, a network of sensor nodes isplaced around fish cages with diameters of up to 40
meters and depths of up to 20 meters, as shown in Fig. 2.
Each node consists of multiple current sensors measuring
current velocities for every depth of choice. In addition
to measuring currents, it also has water quality sensors(dissolved oxygen, temperature, conductivity, pH, etc.) at
multiple depths. It has at least one underwater camera toobserve the fish movements, especially during feeding.
Each node is designed to have a capacity for energyharvesting– solar, wind, tidal, or other sources – so thatit can operate continuously off-grid. A computer above
surface performs corresponding calculations on thesensor readings to obtain the measurements before
timestamping them.
In each cage, sensor nodes communicate in a star Wi-Fi
network, where one node is designated as the master andthe rest as slaves. Slaves send their measurement data to
the master, which may or may not have its own sensors
for collecting measurements. Data sent to the master aretransmitted either directly to the onsite farmer’s device,
which is connected to the master, or to a super master,which sends these data to a cloud server via 3G, 4G or
5G.
In addition to collecting sensor data, the farmer’s
devices can also send and access various data – fish
information, feeding history, etc. – to and from the cloudserver. In another cage, an automatic feeding machine isconnected to the master which operates at the command

Fig. 1. Top-view concept of changes in underwater currents inrelation to fish feeding behavior. More water flows out of thecage when more fish are feeding at surface (a) and less waterflows when fishes go back to the deeper part of the cage (b).

feeding activity – the depths and speeds at which they are
swimming, and of the decision-making process of anexpert fish farmer. In turn these insights could be usefulfor optimizing the feeding control which could also help
less-skilled farmers improve their feeding decisions.
So far, a group of researchers have proposed a sensor
system for monitoring various measurements in water,
including fish displacement speeds using sensors sensing
fish-generated flow at multiple depths [30]. These speed
sensors were calibrated by current sensors placed outside
the cage. All sensor data were then fused into feeding
control decisions. Using gathered real-world
measurements, they made simulations of feeding
activities in cages using their system and minimized
uneaten feeds in water.
Our research revisits the approach of measuring
underwater currents for fish feeding control in marine
aquaculture farms. By measuring the currents around thefish cage at different depths during feeding activities, we
can estimate their feeding behavior reliably at varying
environment conditions. We can analyze the relationshipof the changes in these measurements with the events infeeding activities, at which the expert farmers wouldchange the amount of feeding. We can then determine themeasurement changes that would optimize the feeding
regimen, thus applying DX on the expert farmers’
knowledge in fish feeding. This paper discusses about theenvisioned design of the sensor system, the developmentof the prototype sensor suite and the initial current
measurement experiment in a fish farm using the said
prototype.

Fig. 2. Design of a sensor system in a fish farm with multiple
sensor nodes, consisting of various sensors, deployed in
multiple fish cages, collecting data for the farmer on ship as
well as for the fishing company during feeding activities.

from the cloud server, which calculates the timing andamount of feeding from the collected data. This machine145

analyze their activity at points of interest with significant
changes in current measurements. Fig. 3a shows the setupof the initial experiment.
Ideally, at least two sets of sensor units should be
deployed so that the other will measure the current goingtowards the fish cage, which would be cancelled from theoutgoing current. Due to time constraints, however, one
set was constructed and used for the initial measurement.
Two or more sets will be deployed for the next
measurement campaigns.

can also send its status to the server so the farmer canperform maintenance on it. Finally, the fishing company
office has access all to data from the farm through its
connection to the cloud server.
3. Sensor Suite for Initial Measurements
3.1. Sensor suite system design
For the initial measurements, two custom-built current
speed loggers were mounted on a metal frame at two
different depths at which fishes were observed before,
during, and after feeding. These sensors measure only
one flow axis given their fixed mounting. An inertial
measurement unit (IMU) logger was mounted closest to
the current sensor of interest. This would measure
movement of the frame caused by fish-induced currents.
These sensors were remotely triggered by Bluetooth tostart logging measurements before they were placed
underwater for hours. Alongside the current sensors were
underwater cameras for visual recording of fish activity,

3.2. Custom-built current sensor
3.2.1. Components and operation
Since initial measurements would be performed, we
decided to develop our own current sensors for lower
development costs. The sensor developed for thisexperiment was a modified propeller-type flow sensor
originally intended for measuring water flow throughwater pipes, as shown in Fig. 3b. Its Hall-effect sensor

Fig. 3. Design of the initial farm experiment using the prototype sensor suite consisting of current, camera, and IMU sensors (a). The
current sensors (b) and the IMU sensor (c) were designed to perform and log measurements underwater during a feeding activity.

generates pulses proportional to the magnetic propeller’srotation.

which were switched to operate before getting
submerged underwater. These observations were used to146

3.2.2. Flow sensor cross calibration
For accurate measurements, the developed flow sensor
was cross-calibrated with a digital clamp-on type flow
sensor (Keyence FD-Q32C). Both sensors wereconnected to an elevated water source where flow waspartially controlled, as velocity and flow rate weredependent on the height, and subsequently the volume, of
the water in the container, as shown in Fig. 4.
Calibration was done by correlating the pulse frequency
to the speed of water through the custom sensor, which
was calculated by measuring the flow rate through thedigital sensor. This was calculated using the relationshipbetween the flow rate and speed of a fluid through a pipe
and the continuity equation, which is given in thefollowing equation:

Fig. 4. Calibration test setup of the current sensor where it isconnected to a digital flow sensor as they both measure waterflow from the container

An Arduino microcontroller would count these pulses
for a given period, obtain the average frequency
throughout the period, and then calculate the current
speed using a calibration coefficient. It would then add
timestamping to the reading and store it in a microSD
card with its datalogging shield with SD and RTC
capability. A Bluetooth module was also connected so it
could be triggered remotely by a computer to start andstop measurements.
Each current sensor was powered by a 9-volt supply (sixAA batteries in series) with a capacity of 2700 mAh. A
power endurance test was made by allowing it to operatecontinuously while being powered by the batteries.
Result showed that it can collect measurements reliably
for around 22 hours.
The flow sensor was not originally designed to bewaterproof, and reinforcements were therefore made bypermanently sealing its electronics enclosure and by
replacing its original cable with a waterproof rubber
molded cable. Other unit components were housed in an
IP68 enclosure.
The mounting for the sensors was a 4-meter aluminum
structure with four legs to which the sensors wereattached to. The sensors depth was adjustable by slidingthem through the legs. For the initial and the succeeding
experiments, it was decided to position the two sensors
three meters apart. In the prototype, they were fixed at
0.7 m and 3.7 m from the top of the frame, respectively.
Each leg was made of three four-meter frames. A crosslike reinforcement was attached at the center of the frame
to minimize bending and to enable the mounting towithstand the underwater currents.

𝑣𝑣𝐹𝐹 =

𝑣𝑣𝐷𝐷𝐷𝐷 𝐴𝐴𝐷𝐷𝐷𝐷
𝑞𝑞𝐷𝐷𝐷𝐷 1000
��
�
=�
𝐴𝐴𝐹𝐹
𝐴𝐴𝐹𝐹
60

(1)

In this equation, v is the water speed, A is the crosssection area of the sensor pipe, and q is the flow rate.
Subscripts F and DF denote the custom and the digitalsensors, respectively. Regardless of the difference of thecross-section areas of the two sensors, that of the digitalsensor is cancelled out in the equation. The constant at
the right converts the units from flow rate (L/min) to
speed (cm/s).
To select the best averaging period to be used inmeasurements, three periods were selected for calibration– 1, 5, and 10 seconds. Readings at 1 second were foundto be discrete as the microcontroller count discrete
number of pulses per second. Range of readings from thedigital sensor were classified as the discrete readings
from the custom sensor. Readings using the 10-second
configuration were more continuous. However, there
were higher chances of averaging high and low sample
values, which may not properly represent the actual measurement. Using the 5-second period seemed to be afavorable configuration as there were perceived smaller
chances of samples with large differences, while its
readings were still continuous. This was therefore theselected configuration for the upcoming experiments.
3.3. Underwater IMU sensor and cameras
The IMU sensor was a Sparkfun 9DoF Razor IMU M0,
a very compact microcontroller with an MPU-9250 IMU
and a µSD card slot onboard. Its IMU consists ofaccelerometer, gyroscope, and magnetometer sensors,

147

The measurement data was post-processed by
calculating the moving average of the readings from thelast 30 seconds of measurements. This is to smoothen theplot for easier understanding of the measurements. From
this point throughout the discussion, the tern“measurements” and “readings” refer to the moving
averages. Noted observations were marked on the plot
according to the time they were recorded. The changes inmeasurements were also analyzed with the video
recordings of fish activity at the time of the noted
observations. It was noted in both measurements that the
spikes before and after a series of zero readings indicate
the time the sensors were deployed and recovered.
In the first fish cage, feed was started to be given insmall amounts at around 11:34, seven minutes after the
deployment of sensor, to attract the fishes to the surface,
and was dispensed continuously at around 11:43, nine
minutes later, as shown in Fig. 5a. Before feeding began,
video recordings showed no presence of fish at thesurface, as shown in Fig. 6a. They were seen shoaling
below, as seen from the second camera. At the start offeeding, measured current at the surface did not increaseright away. Fishes started schooling and few started
swimming to the surface only at around 11:36, as shownin Fig. 6b. The number of fish swimming to the surface
continued increasing before the feeds started floating
away from the cage, blurring the surface camera ataround 11:42. Despite this observation, current readings
did not indicate significant increase in current.
Significant increase in surface current started at 11:43.
At around this time, the fisherman also started dispensing
feed to the cage continuously. It was also around this timethat the fishes at 3.4 meters depth became significantly
less visible. This could be attributed to the decrease inillumination due to the increased fish activity at thesurface blocking more light, as well as to dispersion oflight from splashing. At around 11:47, vigorous fish
feeding was observed from the surface. This was also
observed from the surface underwater camera, as shownin Fig. 6c, although with difficulty due to blurred water.
This could be partially attributed to the single lensproperty of the cameras, therefore lacking the ability toadjust its focus to infinity. Movement from fishes atsurface appear to be slightly faster than fishes at 3 meters
below. From this point towards the end of the video, thevisibility of fishes would back-and-forth increase anddecrease. Due to insufficient battery power, theunderwater cameras stopped recording at 12:09 (surface)

and is therefore capable of measuring linear acceleration,
angular rotation velocity, and magnetic field vectors.
With a Real-Time Clock (RTC) attached, this board wasable to timestamp its measurements before writing them
to a 32 GB µSD card. Powered by a 1000-mAh lithiumion polymer (Li-Po) battery, it was enclosed in an IP68
enclosure, as shown in Fig. 3c. Power endurance test
result showed that this sensor could collect data foraround 22 hours as well.
Two GoPro cameras were mounted on the frame
alongside the current sensors. Each had a 512 GB µSD
card to be able to record as much fish activity as possible.
Fully charged, each camera can capture videos for around80 minutes, around the same amount of time feeding is
made.
4.

Initial fish farm current measurements

4.1. Experiment overview
On March 18, 2021, we performed the initial current
measurement experiment at a fish farm located in Usuki
City in Oita Prefecture. Measurements were made at two
100 m2 square cages with depth of 7.5 meters whilefeeding operations were done by the farmer. These cages
contained around 3500 yellowtail amberjack (Seriolalalandi) fishes, locally known as Hiramasa. The fishes
had been raised for a year and each weighed around 3
kilograms. Each feeding operation lasted around 80
minutes.
The sensors were powered on, closed, and triggered torecord data before submerging them for measurement. As
shown in Fig. 3a, the sensor frame was then mounted on
one side of the fish cages. The cage frame on which thesensor frame was mounted on was elevated at 0.3 m
above water. The sensors were therefore positioned at
depths of 0.4 m and 3.4 m, respectively. This experiment
provided insight for validating or improving the designof the proposed sensor system.
4.2. Results and Discussion
Due to human error, the IMU was left open before
submerging in a trial measurement before themeasurement of the target cages, permanently damaging
it without having collected any data. As for the video
recordings, due to insufficient battery power from trial
recording, only the first half of the feeding at the firstcage had underwater recordings. For the second cage,
video recording above surface was taken instead.

148

Fig. 5. 30-second moving average of current measurements at the first fish cage (a) and at the second cage (b). Feeding activity in both
cages lasted for around 80 minutes, not including sensor deployment.

reaching over 4 cm/s. The fisherman switched thefeeding machine to dispense continuously at 13:20. The
fishes were observed to be actively swimming at thesurface one minute later, with current readings exceeding
6 cm/s.
The measurements at 3.4 m depth in the second cagewere also generally lower than those at 0.4 m depth. But
unlike in the first cage, there was an increasing trend in
the surface measurements at the second cage from, witha few drops throughout the continuous feeding. Current
measurements peaked at 13:51, at 14:04, and at 14:09,
exceeding 16 cm/s. Feeding was ended at 14:20, at whichmeasurement was at around 4 cm/s. Reading eventually
dropped to almost zero after around one minute, just a
few moments away from sensor recovery. At around thistime, surface of the cage became calm, as shown in Fig.
7c.

and 12:04 (3.4 m depth), about halfway of the feeding
activity.
Throughout the feeding, measurements at the surface
were generally higher than those at 3.4 meters. The trend
in measurements throughout the activity was generally
uniform, with peak values ranging within 4-10 cm/s. Low
feeding activity on that day was noted by the farmer.
Current reading was at 4.51 cm/s when the fisherman
stopped dispensing feeds at 12:56. It was noted that fishes
were still swimming around the surface at that time. One
minute later, measurements dropped to almost 0 cm/s, thesame time it was noted that fishes were no longer visible
from the surface, indicating that they swam back to thedeeper part of the fish cage.
In the second cage, the time gradual feeding started was
not noted properly, as it conflicted with the time of sensor
deployment. It could have begun sometime after 13:03,
when the sensors were deployed, as indicated by thesensor readings, as shown in Fig. 5b. At the start of thevideo at around 13:10 (Fig. 7a), few fishes were
swimming up during gradual feeding. Measurements
remained low, up to 2.51 cm/s. At 13:17, while there was
no increase in current yet, feeds started to disperse fromthe cage, as observed in the video. As shown in Fig. 7b,
more fishes started swimming at the surface at 13:19,
with even more splashing observed, and readings149

Fig. 6. Snapshots of video recordings at the first fish cage at the start of measurement (a), at the start of gradual feeding (b), and atcontinuous feeding (c). Feeding activity in both cages lasted for around 80 minutes, not including sensor deployment.

A similar observation for both feeding activities wasthat current measurements were at around 4 cm/s when
feeding was stopped by the fisherman. Both gradually
dropped to almost zero after around one minute. As forswitching from gradual to continuous feeding, it is still
difficult to determine the threshold since currents whencontinuous feeding started at different cages weredifferent. However, it can be observed that increases
were gradual and consistent, eventually exceeding 6 cm/s.

150

currents. The IMU sensor was also developed formeasuring movement of the mounting frame. Both kindsof sensors could record measurements reliably for at least
22 hours.
The sensors were deployed in the two fish cages formeasurements during a feeding operation. Especially at
the surface, changes in current outside the cages wereobserved to correspond to changes in fish behavior andappetite as observed by the farmer, prompting to start andstop feeding. However, more data needs to be collected
to verify this observation.
Future work also includes development of a network of
sensor nodes as described in the system architectureusing more robust current sensors. These are future
research tasks towards implementing DX in fish feedingin marine aquaculture farms.
Acknowledgement
This research is funded by the Japan Science andTechnology Agency through the Adaptable and Seamless
Technology transfer Program through Target-driven
R&D (A-STEP) research grant (No. JPMJTM20GS). We
would like to acknowledge the support of our partnercompany, Belltechne Co., Ltd., in this collaboration forproviding the access to the fish cages for the experiment
as well as for their inputs and support for proper
execution of the experiment. D. B. S. acknowledges the
support of the Ministry of Education, Culture, Sports,
Science and Technology of Japan (MEXT) through the
Monbukagakusho: MEXT scholarship.
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Authors Introduction
Mr. Dominic B. Solpico
Mr. Solpico obtained his M. S.
degree in Electronics Engineering at
the Ateneo de Manila University,
Philippines in 2015. He is currently
taking his Doctoral course at Kyushu
Institute of Technology, Japan. His
research interests are in the fields ofIntelligent Aquaculture, WirelessSensor Networks (WSNs), and Field
Robotics.
Dr. Yuya NishidaDr. Nishida is currently an
Associate Professor at the
Department of Human Intelligence Systems, Kyushu Instituteof Technology, Japan. He
obtained his M. S. degree inEngineering in 2008 and his D.
Eng. degree in 2011 at the same
university. His research interests
are in the field of Underwater Robotics, Field Robotics,
and Intelligent Systems.
Prof. Kazuo Ishii

Prof. Ishii is currently a Professor
at the Department of HumanIntelli-gence Systems of Kyushu
Institute of Technology, Japan.
He obtained his M. S. degree in1993 and his D. Eng. degree in1996 at The University of Tokyo.
His research interests are in thefields of Underwater Robotics, Field Robotics, Neural
Networks and Intelligent Systems.

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