Advanced Technology in Aquaculture – Smart Feeding in Marine Fish Farms

Føre, Martin; Alver, Morten Omholt; Frank, Kevin; Alfredsen, Jo Arve

doi:10.1007/978-3-031-22584-0_9

Cited by 4 publications

(1 citation statement)

References 111 publications

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“…Nutrition: according to Kyriazakis (2023) , the term Smart Nutrition describes the method of feeding animals with the help of smart devices (such as cameras and sensors), that through communication with other devices or networks and/or the use of data analytics and AI, are able to capture information automatically and generate outputs that can be used for the management of livestock systems. The use of these technologies are already described in cattle (Gonzáles et al, 2023; Kyriazakis, 2023 ), poultry ( Zuidhof et al, 2023 ), pigs ( Brossard et al, 2023 ), fish ( Fore et al, 2023 ) and other animals, offering solutions that encompass automated individual feed intake quantification ( Bezen et al, 2020 ), feeding behavior ( Bloch et al, 2023 ), rumination pattern tracking ( Kliemann et al, 2023 ), pasture management and biomass calculation ( Castro et al, 2020 ), among others.…”

Section: Applicationsmentioning

confidence: 99%

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Curti,

Selli,

Pinto

et al. 2023

Anim. Reprod.

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Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.

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Section: Applicationsmentioning

confidence: 99%

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Curti,

Selli,

Pinto

et al. 2023

Anim. Reprod.

View full text Add to dashboard Cite

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Characterization of nutrients and contaminants in fish sludge from Atlantic salmon (Salmo salar L.) production sites - A future resource

Sele,

Ali,

Liland

et al. 2024

Journal of Environmental Management

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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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Advanced Technology in Aquaculture – Smart Feeding in Marine Fish Farms

Cited by 4 publications

References 111 publications

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Characterization of nutrients and contaminants in fish sludge from Atlantic salmon (Salmo salar L.) production sites - A future resource

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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