Robotics for Sea-Based Fish Farming

Kelasidi, Eleni; Svendsen, Eirik

doi:10.1007/978-3-030-89123-7_202-1

Cited by 12 publications

(1 citation statement)

References 25 publications

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“…Deploying large AI models is also demanding in terms of memory requirements, and incorporating these models into small memory chips poses a significant challenge. These challenges can be addressed using smart automation systems that include the integration of artificial intelligence, robotics, Internet of Things (IoT), edge computing, and advanced distributed algorithms for real-time image analysis [87][88][89]. Real-time applications of AI models in aquaculture would include, among others, fish counting, fish monitoring, fish feeding, fish classification (normal or deformed), fish behavior analysis, or fish sorting.…”

Section: Miscellaneousmentioning

confidence: 99%

Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species

Kumar,

Marée,

Geurts

et al. 2023

Biomolecules

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Detecting skeletal or bone-related deformities in model and aquaculture fish is vital for numerous biomedical studies. In biomedical research, model fish with bone-related disorders are potential indicators of various chemically induced toxins in their environment or poor dietary conditions. In aquaculture, skeletal deformities are affecting fish health, and economic losses are incurred by fish farmers. This survey paper focuses on showcasing the cutting-edge image analysis tools and techniques based on artificial intelligence that are currently applied in the analysis of bone-related deformities in aquaculture and model fish. These methods and tools play a significant role in improving research by automating various aspects of the analysis. This paper also sheds light on some of the hurdles faced when dealing with high-content bioimages and explores potential solutions to overcome these challenges.

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Section: Miscellaneousmentioning

confidence: 99%