5. Introduction
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Intelligent fish farming is where we rely more on technology than on
human hands to help us improve fish production.
It helps us to easily monitor the health of
the fish, water quality parameters, optimize
the feeding, better waste management, and
much more.
Two of the key challenges that have held
farmers back from adopting aquaculture
farming in India are the lack of technical
knowledge and efficient technology to monitor
the farms. – Mishra (2022)
Machinery, and new generation of IT such as IoT,
big data, artificial intelligence (AI), 5G, cloud
computing and robots are used for remote
measurement and control of fish farm.
By 2050, food production needs to increase by 60% to
feed a world population of 9.3 billion. With climate
change and its consequent impact, there is
tremendous pressure to meet the rising food security
and nutritional demand. An emerging alternative
solution to this is smart aquaculture. – Somasundaram
(2022)
6. Introduction
Benefits of Automation
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• Reduced costs
• Reduced energy consumption
• Reduced staffing
• Reduced workload
• Increased control
• Increased overview
• Increased security
• Increased fish welfare
9. Sensor
Technology
• Monitors key parameters of water such as temperatures, pH,
Oxidation-Reduction Potential, dissolved oxygen, electronic
conductivity and salinity.
• They can detect the diseases as well.
Temperature sensor:
• It can be easily interfaced with Arduino using one-
wire protocol.
• Probe type sensor is perfect for sensing
temperature of water in wet conditions. The
• temperature range is in between -55° to 125°C.
10. Sensor
Technology
pH sensor:
• A pH sensor is called a pH probe which combines
plastic shell rechargeable acidity meter electrode.
• It is round surfaced, refillable and appropriate for
labs, schools where periodic pH calculation is
done.
Turbidity sensor:
• Light scattered by sold particles which are small
in water is measured by turbidity sensor
Conductivity sensor:
• Electrical conductivity is related roughly to general
hardness (GH) and TDS.
11. Sensor
Technology
Dissolved Oxygen sensor:
• There are two groups of developed sensors:
electrochemical and optical sensors.
Feeding sensor:
• Smart feeding system with the
combination of water vibration-based
sensor, and machine learning; could
detect and determine the appetite of the
fish to avoid overfeeding or
underfeeding.
12. Big data and
cloud
• Big Data and Cloud technologies are being
implemented in precision fish farming to collect its
operation information and data, ensuring precise
control, water quality assurance, efficiency, cost
and labor reduction during the farming process.
• The collected data, upon detailed computing and
processing, can be used to forecast production
(such as feeding cost and harvest quantity) and to
identify potential problems (such as disease
prevention and climate impact), all of which
become important reference for next season’s
farming operations.
13. IoT Technology
• It collects data from smart sensors to satellites, and
crunches it using various cloud-based analytic
software tools, including artificial intelligence, to
make aquaculture operations more efficient and
even eco-friendly.
• For example, iQShrimp software platform uses
machine learning and sensors to give farmers
insight into their operation. The platform collects
data on shrimp size, water quality, feeding patterns
and weather conditions using mobile devices,
sensors, and automated feeders. Combined with
other data, the algorithms go to work to provide
recommendations such as feeding management
strategies and optimal harvest dates.
15. 02
Software
• The sensors will be installed in a fish pond and
connected to a microcontroller for later processing
and the results displayed on the screen.
• Actuators: they control several indicators in response
if there are indications that water quality is beyond
normal limits.
• Actuators can be activated automatically or manually.
Hardware
A. Automation in Ponds
• Software is used to manage programs
on the system.
• Data captured by the sensor will be
converted into data with a standard
unit so that it is easy to read.
• Software is also used to carry out
automatic responses when there are
indications of parameter mismatch,
set the normal limits of each
parameter and turn on the indicator.
18. 1
2
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Hardware
Control system
Machine learning model
It mainly consists of three parts such as the Perception, IoT cloud, and
Graphical user interface (GUI).
The decision regression tree machine learning model is used in the
proposed system to predict the water condition of the Biofloc
container.
B. Automation in Biofloc
Model proposed by Mozumder and Sagar (2021)
Described via Algorithm 1 (on next slide).
27. IoT
Sensors
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29. Sensor Technology
IoT technology
Intelligent information
processing model
System integration
Big data and cloud platform
Equipment digitization,
precision control, and edge
computing
Important aspects of
Intelligent aquaculture
30. 01 04
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31. Sensor Technology
Monitors key parameters of water such as
temperatures, pH, Oxidation-Reduction Potential,
dissolved oxygen, electronic conductivity and
salinity. They can detect the diseases as well.
For example, Smart Water Monitoring System for
fish farm QAM300-DE
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