Improving Aquaculture Systems using AI: Employing predictive models for Biomass Estimation on Sonar Images

Pargi, Mohan Kashyap; Bagheri, Elham; F, Ricardo Shirota; Huat, Khoo Eng; Shishehchian, Farshad; Nathalie, Nathalie

doi:10.1109/icmla55696.2022.00250

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) 2022

DOI: 10.1109/icmla55696.2022.00250

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Improving Aquaculture Systems using AI: Employing predictive models for Biomass Estimation on Sonar Images

Mohan Kashyap Pargi

Elham Bagheri

Ricardo Shirota F

et al.

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A Critical Assessment of the Process and Logic Behind Fish Production in Marine Recirculating Aquaculture Systems

Waller

2024

Fishes

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A recirculating aquaculture system (RAS) represents a forward-looking form of aquaculture. A RAS consists of fish tanks and water treatment processes in a closed loop to sustain the environmental conditions for fish production. However, the rapid industrialization of the technology is fraught with transfer problems. This review justifies a RAS process chain based on fish biology. The underlying concept has been evaluated by the author in experimental and commercial RAS projects. The core idea is that the fish must be considered as a technical subcomponent in a RAS, determining the technology. Fish, when considered as small biological machines, are still a black box in many ways. However, their basic biology and physiology provide all the knowledge to implement them in a technical setting. The information required to understand this concept is presented and discussed based on current scientific knowledge. The conclusion is that the technology is available but needs to be rigorously implemented. If this were carried out, fish production in RASs would be ecologically sustainable, which is already claimed for RASs but is not always the reality in commercial applications.

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A Critical Assessment of the Process and Logic Behind Fish Production in Marine Recirculating Aquaculture Systems

Waller

2024

Fishes

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Artificial Intelligence of Things (AIoT) Advances in Aquaculture: A Review

Huang,

Khabusi

2025

Processes

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The integration of artificial intelligence (AI) and the internet of things (IoT), known as artificial intelligence of things (AIoT), is driving significant advancements in the aquaculture industry, offering solutions to longstanding challenges related to operational efficiency, sustainability, and productivity. This review explores the latest research studies in AIoT within the aquaculture industry, focusing on real-time environmental monitoring, data-driven decision-making, and automation. IoT sensors deployed across aquaculture systems continuously track critical parameters such as temperature, pH, dissolved oxygen, salinity, and fish behavior. AI algorithms process these data streams to provide predictive insights into water quality management, disease detection, species identification, biomass estimation, and optimized feeding strategies, among others. Much as AIoT adoption in aquaculture is advantageous on various fronts, there are still numerous challenges, including high implementation costs, data privacy concerns, and the need for scalable and adaptable AI models across diverse aquaculture environments. This review also highlights future directions for AIoT in aquaculture, emphasizing the potential for hybrid AI models, improved scalability for large-scale operations, and sustainable resource management.

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Improving Aquaculture Systems using AI: Employing predictive models for Biomass Estimation on Sonar Images

Cited by 2 publications

References 19 publications

A Critical Assessment of the Process and Logic Behind Fish Production in Marine Recirculating Aquaculture Systems

A Critical Assessment of the Process and Logic Behind Fish Production in Marine Recirculating Aquaculture Systems

Artificial Intelligence of Things (AIoT) Advances in Aquaculture: A Review

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