Artificial Intelligence and Machine learning (ML) Techniques in Fisheries and Aquaculture

 

                             Artificial Intelligence and  Fisheries Science

                  Artificial Intelligence and Machine learning (ML) Techniques in Fisheries and Aquaculture                

Artificial Intelligence and Fisheries

What is Artificial Intelligence(AI)? 

In
computer science, artificial intelligence (AI) is also known as machine
intelligence, is machine-confirmed intelligence, as opposed to
human-demonstrated natural intelligence for the purpose of not only detecting
captured fish, but also tracking dead zones and sea current and analyzing
emissions.

Classical
thinkers may be drawn to the early phases of contemporary artificial
intelligence to explain human thought as a symbolic framework. Until 1956, the
word “artificial intelligence” was first developed at Dartmouth
College in New Hampshire, USA, in a field of artificial intelligence that was
not well recognized.

Artificial
Intelligence (AI) by extension means ‘the future built from the bits of past’.
There are initiatives that learn new solutions through experience. In a number
of areas, AI has been applied, from agriculture to full automation in
industries. The fisheries sector can expand rapidly through AI and production
can be multiplied in a short period of time as it makes aquaculture a less
labor-intensive sector.

Artificial intelligence (AI) and machine learning (ML) are two of the most talked about and exciting technologies of our time. AI is a field of computer science that focuses on creating machines that can think and act like humans. ML is a subset of AI that involves programming computers to learn from data and make decisions based on what they learn.

When it comes to AI, the goal is to create machines that can think and act like humans. This means that these machines must be able to understand the context of their environment and make decisions based on that understanding. For example, self–driving cars and robotic vacuums are two types of AI–driven machines that are designed to act autonomously. These machines are programmed to “see” their environment and make decisions based on what they “see”.

ML is a subset of AI that focuses on programming machines to learn from data. This means that machines are given a set of data and they are able to “learn” from it. This learning process is often referred to as “training”. For example, a machine can be “trained” to recognize images of cats or dogs by being given a set of images and learning to recognize the differences
application of artificial intelligent and machine learning

AI and ML have a wide range of applications in today’s world. One of the most common applications is in the field of healthcare. AI and ML can be used to develop algorithms that can diagnose diseases and recommend treatments. AI and ML can also be used to monitor patient data and detect any changes in health.

AI and ML are also used in the retail industry. AI–driven chatbots can be used to answer customer queries and provide personalized recommendations. AI and ML can also be used to analyze customer data and provide insights into customer behavior. This enables retailers to customize their communication with customers and increase sales.

AI and ML are also used in the finance industry. AI and ML can be used to detect fraudulent transactions and detect money laundering. AI and ML can also be used to analyze financial data and make predictions about the future of the stock market.

Lastly, AI and ML are being used in the field of autonomous vehicles. AI and ML are used to develop algorithms that can safely navigate roads and avoid obstacles. This technology is being used in self–driving cars and is expected to revolutionize the transportation industry.

In conclusion, AI and ML are two of the most exciting and rapidly developing technologies of our time. AI and ML

AI Tools and Applications in Fisheries

AI and ML can be used in the fisheries industry to improve efficiency and reduce costs. By using AI and ML, fisheries can be better monitored and managed. AI and ML can be used to detect changes in fish populations and detect illegal fishing activities. AI and ML can also be used to detect changes in water temperature and predict changes in fish behavior.

AI and ML can also be used to automate fish farming operations. AI and ML can be used to monitor fish health and detect any signs of disease. AI and ML can also be used to detect changes in water quality and optimize water flow.

AI and ML can also be used to analyze data from fish tags and track fish movements. This can be used to analyze fishing patterns and identify areas of potential overfishing. AI and ML can also be used to detect changes in sea surface temperature and predict changes in fish populations.

In conclusion, AI and ML can be used to improve the efficiency and cost effectiveness of fisheries. AI and ML can be used to monitor and manage fish populations, automate fish farming operations, and analyze data from fish tags. AI and ML can also be used to detect changes in water temperature and predict changes in fish behavior.

    Data
base management system:  

      It is a data collection system of fisheries like as item organized and
in use.

·      Application   software: 

    Artificial
intelligence software is managing the data collected by systematically for use
and compares the data for research.

·   Geographical
information: 

     Production maintenance and updating of
distribution maps of marine species of commercial importance.

·        
Satellite
optical.

·        
E-logbook:
log records which keep track of catches (origin and volume) and gear used.

·        
It is used to reduce waste food by using
sensor to detect hunger and by controlling release the right amount of food.

Uses of Artificial Intelligence

·        
Biomass estimation

·        
Visual health and quality inspection

·        
Pellet detection

·        
Optimizing feed efficiency

·        
Modeling the environmental impact of
fishing activity by regulating the fishing zones

Drones in Aquaculture

·        
Drones also offer application for
aquaculture both above and below the water.

·        
Drones can be utilized for monitoring
offshore fish farms.

·        
Inspecting underwater cages for damage
of holes.

·        
Apium Swarm Robotics use drones to
survey the ocean and provide analysis through the use of sensor technology.

·        
Drones are also able to collect
information (fish stock analysis and environmental tacking) that can be used to
create algorithms that further develop the technology or applications available
in the production of aquaculture.

Umitron Cell

Umitron
cell is smart feeding system for aquaculture, and the world’s first real time
ocean-based fish application detection system.

Routine Checkup of Stocks

Vision-based
sensors on Artificial Intelligence systems allow it conceivable for cultured
animals to analyze swimming habits, height, accidents, etc. Such data is
processed in order to be compared in the future. Xpertsea is an aquaculture
engineering company that compromises an AI system named Xpercount that relates
to weigh, count, photograph, and size shrimp in seconds for machine learning
and camera. This gathered data are analyzed for the periodic health of stock to
be detected.

Robots Farm our fish 

Robotic
cages, called aquapods, such as the sea station by InnovaSea.Aquapod is a free
floating fish farm that can accommodate several hundred or several thousand
fish.

Fish seed screening

Fish
seed screening identification and selection of healthy fish seed is very
important in fish farming. Often it become laborious and need to employ many
workers for screening of healthy fish seed.

Sensors for aquaculture

·        
Drones and robots use sensors to
navigate underwater and collect data such as water pH, salinity, oxygen levels,
turbidity, and pollutants.

·        
Analysis of oxygen level and water
temperature.

Internet of things(IOT)

·        
IOT is the technological revolution of
computing and communications that creates the robot capable of performing
tasks, as assigned by a remote user or that transfers information obtained
through sensors to producers for analysis on smart phones.

·        
Monitoring DO level

·        
Monitoring pH values

·        
Water management.

·        
Monitoring fishes behaviors using Digital
image processing applications

Image processing for monitoring
fishes

·        
Capturing the images of fishes with
water proof cameras.

·        
Enhancing the images using digital image
processing techniques.

·        
Observing the movements, populations of
fishes, their productivity etc.

Great
advancements are being made in mechanized fisheries with the use of artificial
intelligence, with full automation still a long way off. However, fully
engaging in Artificial Intelligence plus robotics will generate considerably
more seafood to feed the rising population while reducing the expense and
environmental footprint. Even though AI is known, full automation is not yet
accessible. Scientists are focusing on technologies that can act in that phase
without human intervention. With almost 95% precision inn operations, AI
aquaculture farms can be operated and managed in a much simpler way. If AI is used
in a proper manner, production of aquaculture products will increase quickly.

                                                       Figure: 1E-Fishery Sensor Technology

Credit of Writing: Last year students of the Faculty of Fisheries- Kashmir ( Mehran, Burhan, Fallah, Rigio, Simrah, Aali gul, Ashutosh,
Jeffrin, Murtaza, Bakhtiyar, Inam, Rizwan, Umer, Arshi, Nazia,  Samreena)

Tags: Artificial Intelligence and  Fisheries Science, AI and Aquaculture .