The seminar presented by Rajesh V. Chudasama at Kamdhenu University highlights the importance and application of automation and artificial intelligence in aquaculture. Key topics discussed include water quality monitoring, feeding management, intelligent aeration systems, and the economic benefits of reducing production costs through technology. While automation offers significant advancements, challenges such as high initial costs and potential system failures remain pertinent concerns for the industry.

1. CREDIT SEMINAR
College of Fisheries Science
Kamdhenu University - Veraval
January - 2023
Department of Aquaculture
Automization in Aquaculture
Presented By,
RAJESH V. CHUDASAMA
Reg. No. 2021010210041063, M.F.Sc.
Under Guidance of,
Dr. N. H. JOSHI
Associate professor, COF, KU.

2. Outline of Seminar
02
• Introduction
• Why Automization?
• Basic Concept of
Automization
• Artificial Intelligence
• Application of
Automization in
Aquaculture
• Water Quality Monitoring
• Intelligent Aeration System
• Feeding Management
• Biomass Estimation
• Fish Health and Welfare
• Future Intervention
• Case Study
• Drawback of Atomization
• Conclusion
• References

3. Automization in Aquaculture
3

4. Rajesh V. Chudasama, Jhanvi M. Tandel,
Nayan A. Zala, Dignati C. Tandel, Poojan H.
Patel and M.D. Shadab Alam (2023).
Automization in Aquaculture – A Short
Review. Biological Forum – An
International Journal, 15(5): 688-698.

5. Aquaculture is the rearing of aquatic
organisms under controlled or semi-controlled
conditions (Stickney, 2022).
Automization is the conversion of a work
process, procedure or equipment to automatic rather
than human control (Newell & Simon, 1956).
Introduction
Image Credit: Davis et al. 2018 04

6. Source: OECD/FAO, 2021; Daoliang LI, 2018
Smallholder Production:
Relying on manual, hand
tools,
experience based farming
Equipment Production:
Automization
Automated Manufacturing:
Using IoT technology to
manage computer software
as the core
Unmaned System:
Artificial intelligence &
robots
19th Century 20th Century 21st Century
Aquaculture 1.0
Aquaculture 2.0
Aquaculture 3.0
“Aquaculture Development Stages”
05
Aquaculture 4.0

7. 0
10
20
30
40
50
60
70
80
90
100
1990 2000 2010 2018 2019 2020
million
tonnes
World Aquaculture Production
Marine
Inland
Year 1990 2000 2010 2018 2019 2020
Inland 12.6 25.6 44.7 51.6 53.3 54.4
Marine 9.2 17.9 26.8 30.9 31.9 33.1
Total Aquaculture 21.8 43.4 71.5 82.5 85.2 87.5
Source: SOFIA, 2022
* Production in million tonnes 06

8. Why Automization?
The cost of production per kilogram in aquaculture is
high mainly due to
• High input cost especially feed cost (60-80%)
• Skill labor requirement
• Higher risk involved
Continuous monitoring requirements of water quality,
biomass, growth etc. (Stickney, 2022).
07

9. AQUACULTURE
FARM
Data Analytics,
Processing
Reporting
Sensors
Evaluate
Observe
Decide & Act
Interpret
Decision
Support
System
Source: Agapiou et al. 2021
Aquaculture Farm Monitoring System of Future
08

10. Basic Concepts of Automization
Aquaculture
Equipment
Intelligent
Equipment
Network
Monitoring
(AI)
Unmanned
Operation
(Robotics)
09

11. Artificial Intelligence
• Artificial Intelligence (AI) is the simulation of human
intelligence by machines and the ability of a computer
program or a machine to think and learn.
• Through AI, fisheries sector can develop rapidly and
production can be double within a short period (Lloyd et
al., 2020).
Intelligence: “The capacity to learn and solve problems.”
10

12. Aquaculture Software Launches
51% of aquaculture software
companies were launched in
the last 5 years.
Source: Eric (2021) 11

13. Source: Crunchbase.com
4.7
44.8
29
22.1
20
17
15.2
12
5.3
100.2
0 20 40 60 80 100
Optoscale
AquaByte
XpertSea
Umitron
Ecto
Aquaconnect
InnovaSeas
Jala Tech
Stingary
eFishery
$ USD (million)
Top 10 AI Software
Companies for Aquaculture
AI
12

14. Source: Eric (2021)
USA, 17%
Norway,
19%
Asia /
Oceana,
26%
Europe,
16%
UK, 10%
Canada,
7%
Africa / Mideast, 5%
For a small country, Norway has a number of
aquaculture software companies.
Location of Aquaculture Software
Providers
13

15. Automization
Feeding Management
Visual Health
Water Quality Monitoring
Biomass Estimation
Application in Aquaculture
14

16. Automization Function PRODUCTION HEALTH WATER
PREDICT
• Forecast
growth
• Predict
disease
outbreaks
• Predict water
quality threats
AUTOMATE
• Estimate
biomass
• Count fish /
Shrimp
• Optimize
feeding
• Count
parasite
• Monitor
disease and
health
• Monitor algae
blooms
• Optimize
aeration
• Automate
quality alerts
Source: Eric (2021) 15

17. • Intelligent fish farm collects water quality information using sensors.
• Regulated by: fertilizing and spraying chemicals on unmanned boat.
• Long time accurate detection of aquaculture water quality parameters provides a
reliable data source for automatic control and intelligent decision-making of
intelligent fish farm (Wang et al., 2021).
Water Quality Monitoring
16

18. Dissolved Oxygen
Glass tube
KCl electrolyte
AgCl electrode
Teflon membrane
Platinum electrode
O ring
The detection of DO mainly includes the
Clark electrode method (Tai et al., 2016).
17

19. Dissolved Oxygen Sensors Advantages
• Fast response time
• Stable measurement results
• Low maintenance (Tai et al., 2016).
DO is accurately controlled by an intelligent aeration system.
18

20. DO
Concentration
Time
Daily manual measure
---- Estimation from manual measure
Example of Dissolved Oxygen Manual Measurement in a
Fish Tank
Source: Marilou, 2021 19

21. Daily Manual Measurement v/s Reality
Daily manual measure
---- Estimation from manual measure
REALITY
DO
Concentration
Time
Source: Marilou, 2021 20

22. Daily manual measure
---- Estimation from manual measure
REALITY
DO
Concentration
Time
Source: Marilou, 2021
Activate The Aerator at The Best Time & Reduce Stress
21

23. Generally most waterproof temperature sensors
can measure within a range of -55°C to +125°C.
It consists of data communication
• power wire,
• ground wire and
• a temperature limit alarm system (Barman et
al., 2015).
Temperature Sensors
Temperature Sensor
22

24. Potentiometric pH measurement.
Usually these sensors are made of pH glass
electrodes with AgCl reference electrodes which is used
to measure the pH value of water (Aakash, 2019).
23
pH Sensor

25. Using UV LED technology, the nitrite/ammonia sensor
measures the concentration of dissolved nitrite/ammonia as
nitrogen in the water.
The sensor measures concentration levels by
nitrate/ammonia dissolved in water by examining its absorption of
ultraviolet (UV) light (Menon and Menon, 2021; Pellerin et al.,
2013).
24
Nitrogen Ion Sensor
UV Sensor

26. A Placement of the Multi
Water-parameter
Monitoring Sensors
SEA-CAGE
Image Credit: YSI Aquaculture Monitoring & Control Technology 25

27. Monitoring system continuously measures several water quality parameters, controls,
connects to a PC, alarms and has powerful desktop software.
Sensor Based Monitoring System
Image Credit: Aeronsystem.com 26

28. User Devise Display
Image Credit: AKVA Observe 27

29. Intelligent Aeration System
• Intelligent aeration system refers to the equipment that can accurately measure and
control the DO in water (Huan et al. 2020).
• The intelligent aerator can monitor water temperature, air humidity, air pressure,
and DO.
• At the same time, it can record scene information by means of video monitoring,
and uploads this information to the cloud platform which can realize the precise
control of the aerator (Wang et al., 2021).
28

30. Feeding Management System
Automatic feeding system in
some developed countries such as
Norway, Japan, and the United States
has entered the application stage, which
has achieved accurate control in the
links of feed transport, storage and
delivery (Wang et al., 2021).
29
Automatic Feeder
Image Credit: Davis et al., 2018

31. The net cage automatic feeding system developed by Norwegian fishery
equipment enterprise consists of management system, online monitoring system
and feeding module.
Cage Feeder
30
Image Credit: fishfarmfeeder.com

32. The robot feeding control system
developed by Finland’s Arvo-Tec company.
Remote control of feeding, water
quality improvement, and precise feeding
using the web interface (Arvotec, 2021).
Robotic Feeder
31

33. Automatic Feeders
Source & Image Credit: Davis et al., 2018 32
Intelligent feeder: Adjusts the feed based on water
quality and weather data, ensures that shrimp get
correct amount of feed intelligently.
Feeding schedules: Feeding schedules can be
configured from smart-phone app. Using mobile
based technology to optimally feed your shrimp.
Reduces FCR: Reduces FCR by 30%, profit margins
will only go upwards!

34. Acoustic Feeding System
Image Credit : Darodes et al. 2021 33
 Appetite Based Intelligent Feeding
 Superior Production Performance
 24 x 7 Feeding System
 Reduced Feed Wastage

35. 34
The hydrophone records the pond soundscape and sends signals to the
controller located either on the feeder or on the shore.
1
Acoustic and feeding data are sent to a computer at the farm’s office at regular
intervals (Tailly et al., 2021; BioRender.com.).
3
The controller then assesses the relative feeding activity and automatically
adjusts the feeding ratio.
2

36. Before Feeding
Feeding
After Feeding
Camera View in Feeding Zone
The number of fish under the camera view (i.e., feeding zone)
drastically increases right after fish food is released, and reaches to its peak
and then decreases after they consumed the fish food (Alexkychen, 2022).
35

37. ADVANTAGES
Acceleration Based
Sensor
Understand Fish
Behavior
Stop Feeding When
Full
Homogeneous growth
1kg of fish feed =
1kg of growth
Less pollution
36

38. Shrimp Auto-Feeder with Intelligent Feeding Sensor

39. Biomass Estimation
• Machine vision based on visible light
• Machine vision based on infrared light
• Acoustics based methods
38

40. The Visible Counter System
• Mass measurement: length-weight & area-weight
• Counting: Area counting & object tracking
• Pretreatment: Calibration
• Feature extraction: size, colour, texture,
Image getting
(Camera)
Image
processing
Statistical
analysis
Source: Li and Duan, 2020 39

41. AI: Shrimp Snap
• Automated feed tray to capture the
images of shrimp.
• Shrimp size and distribution are
analyzed images.
• Disease symptoms are identified via
computer vision.
Source: Huang, 2018 40

42. FULL
Healthy and well
fed!
PARTIAL
Insufficient feed
or unhealthy
Unhealthy or
severe lack of feed
EMPTY
Artificial Intelligence Underwater Monitoring for Shrimp Farming
Shrimp Intestine (gut) Observation
0
100
Average
Intestine
length
Unhealthy or
Lack of Feed
Source: Huang, 2018 41

43. B
I
O
M
A
S
S
E
S
T
I
M
A
T
I
O
N
Image Credit: Tonachella et al., 2022 42

44. The Infrared Counter System
Scanner Unit
Control Unit
Computer
Source: Li and Duan, 2020 43

45. • Infrared light is an electromagnetic wave whose wavelength 760 nm.
• With advances in computer technology, machine vision based on infrared light has
developed rapidly, which has been used to count fish in aquaculture.
• It provides a counting fish and analyzing behavior, which is relatively simple and
plays an important role in the development of effective method for fish biomass
estimation (Daoliang Li et al., 2019).
44

46. Acoustics Based System
Signal source Transmitter Transmitting array
Analysis Receiver Receiving array
Emission signal
Object
Echo
Acoustic Transmissions
Source: Li and Duan, 2020 45

47. AI: Fish Health and Welfare
Aims to detect and diagnose fish diseases in fish farms automatically.
Connected to sensors, camera, and a personal computer (PC). The proposed
system is presented in three consequent stages (Waleed et al., 2019).
46

48. FIRST STAGE
Water Quality
Examination
Capture Images
+
SECOND STAGE
All Inputs Received
in System
Data Processing
Detection of
Abnormality
Segmentation and
Classification
+
+
+
THIRD STAGE
Notification to Farmer
SMS
LCD
47

49. Source: Abinaya et al. 2021
Image
Processing and
Detection of
Abnormal Fish
48
Original Image
Identified Image
Segmented Image

50. Detection of Lice
(Parasite)
Source: AWS Events, 2019
49

51. CAPTURE FOOTEG PROCESS FOOTAGE DISPLAY DATA
1
2
3
4
5
Fixed Moving Adult Female
No.
lice
per.
fish
Types of Lice
Lice Counting
Source: AWS Events, 2019 50

52. Future Interventions
Underwater Drone
Image credit: thefishsite.com 51

53. Remotely Operated Vehicles
(ROVs)
• Net and mooring inspection
• Effective cleaning of cage net
• Monitor the feeding process,
stock health and observe fish
behavior.
Source: Freelancer, 2016
52

54. Virtual Reality (VR)
The eyes of the next generation to Aquaculture
• The opportunities for VR in the aquaculture industry are: Training and Education
• Allow students to virtually visit a fish farm.
• VR is being used by The Norwegian University of Science and Technology (NTNU)
• The program has been designed to teach about fish welfare, disease prevention, escaping fish
and dangerous working conditions.
53

55. CASE STUDY
54

56. 55
Objective: The intelligent fish farm tries to deal with the precise work of increasing
oxygen, optimizing feeding, reducing disease incidences, and accurately harvesting
through the idea of “replacing human with machine”.
Method: Application of fishery intelligent equipment , IoT, Computing
Results: The AI to measurement fish and control, feeding, inspection and
harvesting process can be inform.
Conclusion: Liberate the manpower and realize green and sustainable aquaculture
CASE STUDY

57. Chennai-Based Startup (Aquaconnect).
The startup has a network has about 4200+ farmers in Andhra,
Tamil Nadu, and Gujarat.
Now, 1350+ farms have been made smart farms with the Farm
MOJO implementation.
FarmMOJO offers complete AI assistance to farmers
throughout the culture, from stocking to harvest (TOI, 2022).
India
56

58. 57

59. • High Costs of Creation
• Unemployment
• A failure of sensor or other components can lead to catastrophic error and crop loss, so
models that are more robust need to be developed to achieve a fully unmanned operation
system.
• Maintenance of system has high cost
Drawback of Atomization
58

60. Conclusion
Environmental
gains
Economic
gains
A Real Time Wireless System
Water quality Fish health & welfare Growth performances
59

61. Even though aquaculture has been practiced for 4,000 years, the sector is still
new and expanding. This industry is majorly dependent on manual operation, high
feed cost, disease risk and labour requirement which has resulted in higher
production cost per capita. However, modern internet based technology and the use
of autonomous machinery has the capacity to not only decrease the cost of
production for farmers, but also has less of an impact on the environment which will
lead to economic gains for the farmers. Modern internet based technology and
autonomous machinery will also contribute in the long term sustainability in
aquaculture industry.
“The Future Made from the Pieces of
Past” 60

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Alammar, M. M., & Al-Ataby, A. (2018). An Intelligent Approach of the Fish Feeding System. Department of Electrical
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Arvotec, (2021). Fish feeding robot. Available online: https:// www. arvot ec. fi/ feedi ng- techn ology/ feedi ngrobot. Accessed
15 Dec 2021
Barman, P., Partha, B., Mondal, K. C., & Mohapatra, P. K. D. (2015). Water quality improvement of penaeus monodon culture
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67. Thank You !!
Automization in Aquaculture