Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Bravo, F (Author) Other; Amorim, J (Author) Other; Amirkandeh, MB (Author) Other; Bodorik, P (Author) Other; Cerqueira, V (Author) FEUP; Gomes, NR (Author) Other; Korus, J (Author) Other; Oliveira, M (Author) Other; Parent, M (Author) Other; Pimentel, J (Author) Other; Reilly, D (Author) Other; Sclodnick, T (Author) Other; Grant, J (Author) Other; Filgueira, R (Author) Other; Whidden, C (Author) Other; Torgo, L (Author) Other

Conference proceedings International

Title: Oceans Conference Record (IEEE)

Pages: 1-6

OCEANS 2024 - Singapore, OCEANS 2024

Singapore, 15 April 2024 through 18 April 2024

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-017-ME4

DOI: 10.1109/oceans55160.2024.10754571

Abstract (EN): The aquaculture industry faces significant challenges related to sustainability, productivity, and fish welfare. Key issues include managing environmental conditions, disease, pests, and data integration from various sensors and monitoring systems. The BigFish project aims to address these challenges through advanced analytics and machine learning, focusing on three case studies in Atlantic salmon farms: predicting oxygen levels, reducing sea lice infestations, and improving data interaction and visualization. Predictive models for oxygen levels and sea lice infestation, as well as natural language interfaces for data visualization, demonstrate the potential for improved decision-making and management practices in aquaculture. Early results indicate the effectiveness of these approaches, highlighting the importance of data-driven solutions in enhancing industry sustainability and productivity. © 2024 IEEE.

Language: English

Type (Professor's evaluation): Scientific