Pattern detection and prediction using deep learning for intelligent decision support to identify fish behaviour in aquaculture

Shreesha, S.; Pai, Manohara M M; Pai, Radhika M.; Verma, Ujjwal

doi:10.1016/j.ecoinf.2023.102287

Ecological Informatics

2023

DOI: 10.1016/j.ecoinf.2023.102287

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Pattern detection and prediction using deep learning for intelligent decision support to identify fish behaviour in aquaculture

S. Shreesha,

Manohara M M Pai,

Radhika M. Pai

et al.

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2024

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Cited by 2 publications

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A deep learning-based biomonitoring system for detecting water pollution using Caenorhabditis elegans swimming behaviors

Kang,

Jeong,

Lim

2024

Ecological Informatics

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A deep learning-based biomonitoring system for detecting water pollution using Caenorhabditis elegans swimming behaviors

Kang,

Jeong,

Lim

2024

Ecological Informatics

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Overview of aquaculture Artificial Intelligence (AAI) applications: enhance sustainability and productivity, reduce labor costs, and increase the quality of aquatic products

Ragab,

Hoseinifar,

Doan

et al. 2024

Annals of Animal Science

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The current work investigates the prospective applications of Artificial Intelligence (AI) in the aquaculture industry. AI depends on collecting, validating, and analyzing data from several aspects using sensor readings, and feeding data sheets. AI is an essential tool that can monitor fish behavior and increase the resilience and quality of seafood products. Furthermore, AI algorithms can early detect potential pathogen infections and disease outbreaks, allowing aquaculture stakeholders to take timely preventive measures and subsequently make the proper decision in an appropriate time. AI algorithms can predict ecological conditions that should help aquaculture farmers adopt strategies and plans to avoid negative impacts on the fish farms and create an easy and safe environment for fish production. In addition, using AI aids to analyze and collect data regarding nutritional requirements, nutrient availability, and price could help the farmers to adjust and modify their diets to optimize feed formulations. Thus, using AI could help farmers to reduce labor costs, monitor aquatic animal’s growth, health, optimize feed formulation and reduce waste output and early detection of disease outbreaks. Overall, this review highlights the importance of using AI to achieve aquaculture sustainability and boost the net profits of farmers

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Pattern detection and prediction using deep learning for intelligent decision support to identify fish behaviour in aquaculture

Cited by 2 publications

References 46 publications

A deep learning-based biomonitoring system for detecting water pollution using Caenorhabditis elegans swimming behaviors

A deep learning-based biomonitoring system for detecting water pollution using Caenorhabditis elegans swimming behaviors

Overview of aquaculture Artificial Intelligence (AAI) applications: enhance sustainability and productivity, reduce labor costs, and increase the quality of aquatic products

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