Role of Artificial inteligence in fisheries and aquaculture ?

Artificial intelligence (AI) can play a significant role in fisheries and aquaculture by providing solutions to various challenges faced by the industry. Here are some examples:

Stock assessment: AI can help in accurately estimating the population size and distribution of fish species, which is essential for sustainable management of fisheries. Machine learning algorithms can analyze data from various sources, including satellite imagery, acoustic sensors, and catch reports, to predict the abundance and migration patterns of fish populations.

Disease detection: Aquaculture is vulnerable to diseases that can spread rapidly and cause significant economic losses. AI-based systems can monitor water quality, feed intake, and other parameters to detect early signs of disease outbreaks. Machine learning models can also analyze images of fish to identify visual cues that indicate the presence of specific diseases.

Feed optimization: Efficient feeding practices are crucial in aquaculture to maximize growth while minimizing waste. AI-based systems can analyze data on fish behavior, feeding patterns, and environmental conditions to optimize feed composition, timing, and quantity.

Harvesting efficiency: AI-based systems can improve the efficiency of harvesting operations by predicting the optimal time and location for fishing based on factors such as weather conditions, fish behavior, and market demand. Automated systems can also sort and grade fish based on their size and quality, reducing labor costs and improving product consistency.

Fraud detection: The seafood industry is prone to fraud, with mislabeling and misrepresentation being common practices. AI-based systems can analyze DNA samples to verify the species and origin of fish products, preventing fraudulent labeling and ensuring compliance with regulations.

Overall, AI has the potential to revolutionize fisheries and aquaculture by providing more efficient and sustainable ways to manage and operate these industries.

Ahmad Al Khraisat

@all Artificial intelligence (AI) is playing a significant role in enhancing and optimizing various aspects of fisheries and aquaculture. Here are some key areas where AI is making an impact:

Data analysis and prediction: AI algorithms can analyze large volumes of data collected from sensors, satellites, and other sources to provide valuable insights. It can help in predicting fish behavior, optimizing feeding strategies, identifying optimal harvest times, and predicting disease outbreaks.

Stock assessment and monitoring: AI can analyze data on fish populations, including size, age, and abundance, to assess stock health. This information helps fisheries managers make informed decisions about catch limits, conservation efforts, and sustainable fishing practices.

Aquaculture management: AI can assist in optimizing aquaculture operations by monitoring water quality parameters, such as temperature, pH, and oxygen levels, in real-time. It can also analyze feeding patterns, growth rates, and environmental conditions to improve feed efficiency and reduce waste.

Disease detection and prevention: AI algorithms can analyze data from various sources, including images, videos, and sensor data, to identify signs of disease or stress in fish. Early detection can lead to timely interventions, reducing the spread of diseases and minimizing losses.

Automated image recognition: AI-powered image recognition technology can automate the identification and classification of fish species, which is crucial for stock assessment, species management, and compliance with regulations.

Autonomous systems: AI can enable the development of autonomous underwater vehicles (AUVs) and unmanned aerial vehicles (UAVs) for data collection, monitoring, and surveillance in fisheries and aquaculture. These systems can help in mapping habitats, monitoring fishing activities, and conducting surveys in remote or hazardous areas.

Decision support systems: AI can provide decision support tools that integrate data from multiple sources and assist fisheries managers, policymakers, and aquaculture operators in making informed decisions. These systems can optimize resource allocation, improve sustainability, and enhance operational efficiency.

It's important to note that while AI brings numerous benefits, its implementation should be accompanied by appropriate data privacy, ethical considerations, and regulatory frameworks to ensure responsible and sustainable use in fisheries and aquaculture.

Asaf Pras

AI can be used in various ways in any water-related environment. Obviously, it can be used to optimize processes, monitor different parameters and factors (check out my research section about "Nowcasting of fecal coliform presence using an artificial neural network" as an example), and more.

It can be the same regarding fisheries and aquacultures - monitoring, optimizing processes, predicting affecting factors, etc. If you are looking to apply some AI techniques and have data - feel free to DM :)

Arindam Kumar Paul

Deploying artificial intelligence in the fisheries is not a very good idea because you need to automatic everything about your plant and surroundings in a way so that the sensors can receive data automatically and send them to a computer to process them and make some decisions so that they can make you some profits. This is like using a high-end Powerful sniper to kill a monkey.

You can consider using artificial intelligence or any other model for market analysis and inventory optimization for your plant. You may also use some image data of your plant to predict the overall station of your plant. I think this is a better idea to use artificial intelligence in fisheries plants when it is bounded into research and development.

If you want to consider the real kiss scenario then I would like to say that mathematics and Statistics are enough for what you need to manage a fisheries or aquaculture system. It is not any application area of artificial intelligence so if you want to do your need to research on it and develop the methods and process yourself.

Paulraj Jawahar

Recently it is reported that AI has been evolved to assess shrimp survuval, basic diseases, stress condition, water quality and feed adjustments in coastal aquaculture system that could enhance survival and reduce feed input cost.

Arindam Kumar Paul

I do agree with the report. Actually, AI/ML technologies are being used in very limited areas for solving problems to it's maintaining cost, uncertainty, lack of interpretability and etc. It is not possible to apply AI to Fisheries practically to optimize cost and maximize outcomes because the cost of maintaining an AI will be huge. Especially when there exists some proven method to solve the problem efficiently AI is not for those till now. ,

As an example, If anyone wants to hire a Pure Mathematician to solve a data science problem where his role is to identify and explain data patterns, it'll be nothing but a waste of money and time. Mathematicians never produce any practical solution in the case and the solution from the mathematician will not be understandable by the team members. Mathematicians can enjoy solving problems where there is no data at all.

I hope I was able to convey my points.

Sina Jamalzadegan

Hi, please find this recent research applying PCA as unsupervised machine learning method for early plant diseases detection:

Article Abaxial leaf surface-mounted multimodal wearable sensor for ...

This ML method could be useful for your data analysis to reduce the dimension of your data and also cluster them!

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