Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming
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.
Authors
- Nuno Gomes (ORCID: https://orcid.org/0000-0003-4864-9530)
- Marianne I. Parent (ORCID: https://orcid.org/0000-0003-4195-3185)
- Vítor Cerqueira (ORCID: https://orcid.org/0000-0002-9694-8423)
- Peter Bodorik (ORCID: https://orcid.org/0000-0002-4121-1942)
- Mariana Oliveira (ORCID: https://orcid.org/0000-0001-9708-1123)
- Chris Whidden (ORCID: https://orcid.org/0000-0001-9406-730X)
- Derek F. Reilly (ORCID: https://orcid.org/0000-0003-4960-8757)
- Tyler Sclodnick (ORCID: https://orcid.org/0009-0002-5204-688X)
- João Pimentel (ORCID: https://orcid.org/0000-0002-5794-3451)
- Joana Amorim (ORCID: https://orcid.org/0000-0003-1359-2095)
- Luı́s Torgo (ORCID: https://orcid.org/0000-0002-6892-8871)
- Ramón Filgueira (ORCID: https://orcid.org/0000-0002-3332-8649)
- Jennie Korus (ORCID: https://orcid.org/0009-0000-1915-2563)
- Jon Grant (ORCID: https://orcid.org/0000-0003-3857-1848)
- Francisco Bravo (ORCID: https://orcid.org/0000-0002-4242-5731)
- Melika Besharati Amirkandeh
Institutions
- Dalhousie University (CA)
- Universidade do Porto (PT)
- Adolfo Ibáñez University (CL)
- University of Prince Edward Island (CA)
- Institut d'Estudis Espacials de Catalunya (ES)
Publication Details
- Published
- 2024-09-23
- DOI
- https://doi.org/10.1109/oceans55160.2024.10754571
- Citations
- 1
- Primary Topic
- Water Quality Monitoring Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.20