analiza aiqbench
This chapter details the application of specific ML architectures, including CNNs and GNNs, to solve core aquaculture challenges like biomass estimation and species recognition. By integrating these models with IoT data, the work demonstrates a pathway toward more sustainable and efficient fish farming operations through automated decision-making.
Machine Learning in Fish Farming
arXiv:2609.13919v1 Announce Type: new Abstract: This chapter explores how machine learning (ML) is transforming aquaculture, with a particular focus on enhancing decision-making processes and improving operational efficiency. The chapter is structured to first introduce the challenges in aquaculture and the role of AI and then provide an overview of ML techniques in the context of aquaculture, followed by applications, emerging trends, future directions, and case studies. The focus is on real-world applications of ML techniques, including Random Forest, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), as well as emerging technologies such as Graph Neural Networks (GNNs) and large language models (LLMs). Key applications include biomass estimation, species recognition, behavioural analysis, and environmental forecasting. The chapter also highlights the synergy between ML and the Internet of Things (IoT) for real-time monitoring and decision support. Ultimately, ML-driven innovations have the potential to revolutionise fish farming, leading to more efficient, sustainable, and productive practices in the aquaculture industry.
Abstract:This chapter explores how machine learning (ML) is transforming aquaculture, with a particular focus on enhancing decision-making processes and improving operational efficiency. The chapter is structured to first introduce the challenges in aquaculture and the role of AI and then provide an overview of ML techniques in the context of aquaculture, followed by applications, emerging trends, future directions, and case studies. The focus is on real-world applications of ML techniques, including Random Forest, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), as well as emerging technologies such as Graph Neural Networks (GNNs) and large language models (LLMs). Key applications include biomass estimation, species recognition, behavioural analysis, and environmental forecasting. The chapter also highlights the synergy between ML and the Internet of Things (IoT) for real-time monitoring and decision support. Ultimately, ML-driven innovations have the potential to revolutionise fish farming, leading to more efficient, sustainable, and productive practices in the aquaculture industry.
Machine Learning (cs.LG)
arXiv:2609.13919 [cs.LG]
(or arXiv:2609.13919v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.13919 arXiv-issued DOI via DataCite (pending registration)
O'Donncha, F. et al. (2026). Machine Learning in Fish Farming. In: O'Donncha, F., Fore, M., Grant, J. (eds) Digital Twin for Marine Fish Farming. Aquatic Production Systems. Springer, Cham
https://doi.org/10.1007/978-3-032-10362-8_3 DOI(s) linking to related resources
Text integral sub licența deschisă a publicației · 205 cuvinte
