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

Francisco Bravo; Joana Amorim; Melika Besharati Amirkandeh; Peter Bodorik; Vitor Cerqueira; Nuno R.C. Gomes; Jennie Korus; Mariana Oliveira; Marianne Parent; João Pimentel; Derek Reilly; Tyler Sclodnick; Jon Grant; Ramón Filgueira; Christopher Whidden

doi:10.1109/OCEANS55160.2024.10754571

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

Francisco Bravo, Joana Amorim, Melika Besharati Amirkandeh, Peter Bodorik, Vitor Cerqueira, Nuno R.C. Gomes, Jennie Korus, Mariana Oliveira, Marianne Parent, João Pimentel, Derek Reilly, Tyler Sclodnick, Jon Grant, Ramón Filgueira, Christopher Whidden

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	OCEANS 2024 - Halifax, OCEANS 2024
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331540081
DOIs	https://doi.org/10.1109/OCEANS55160.2024.10754571
State	Published - 2024
Externally published	Yes
Event	OCEANS 2024 - Halifax, OCEANS 2024 - Halifax, Canada Duration: 23 Sep 2024 → 26 Sep 2024

Publication series

Name	Oceans Conference Record (IEEE)
ISSN (Print)	0197-7385

Conference

Conference	OCEANS 2024 - Halifax, OCEANS 2024
Country/Territory	Canada
City	Halifax
Period	23/09/24 → 26/09/24

Keywords

advanced analytics
aquaculture
data visualization
machine learning
sustainability

Access to Document

10.1109/OCEANS55160.2024.10754571

Cite this

Bravo, F., Amorim, J., Amirkandeh, M. B., Bodorik, P., Cerqueira, V., Gomes, N. R. C., Korus, J., Oliveira, M., Parent, M., Pimentel, J., Reilly, D., Sclodnick, T., Grant, J., Filgueira, R., & Whidden, C. (2024). Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming. In OCEANS 2024 - Halifax, OCEANS 2024 (Oceans Conference Record (IEEE)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/OCEANS55160.2024.10754571

@inproceedings{e72fc54ee42b4cfdbd93a93bf4e1b53f,

title = "Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming",

abstract = "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.",

keywords = "advanced analytics, aquaculture, data visualization, machine learning, sustainability",

author = "Francisco Bravo and Joana Amorim and Amirkandeh, {Melika Besharati} and Peter Bodorik and Vitor Cerqueira and Gomes, {Nuno R.C.} and Jennie Korus and Mariana Oliveira and Marianne Parent and Jo{\~a}o Pimentel and Derek Reilly and Tyler Sclodnick and Jon Grant and Ram{\'o}n Filgueira and Christopher Whidden",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; OCEANS 2024 - Halifax, OCEANS 2024 ; Conference date: 23-09-2024 Through 26-09-2024",

year = "2024",

doi = "10.1109/OCEANS55160.2024.10754571",

language = "English",

series = "Oceans Conference Record (IEEE)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "OCEANS 2024 - Halifax, OCEANS 2024",

}

Bravo, F, Amorim, J, Amirkandeh, MB, Bodorik, P, Cerqueira, V, Gomes, NRC, Korus, J, Oliveira, M, Parent, M, Pimentel, J, Reilly, D, Sclodnick, T, Grant, J, Filgueira, R & Whidden, C 2024, Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming. in OCEANS 2024 - Halifax, OCEANS 2024. Oceans Conference Record (IEEE), Institute of Electrical and Electronics Engineers Inc., OCEANS 2024 - Halifax, OCEANS 2024, Halifax, Canada, 23/09/24. https://doi.org/10.1109/OCEANS55160.2024.10754571

Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming. / Bravo, Francisco; Amorim, Joana; Amirkandeh, Melika Besharati et al.
OCEANS 2024 - Halifax, OCEANS 2024. Institute of Electrical and Electronics Engineers Inc., 2024. (Oceans Conference Record (IEEE)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming

AU - Bravo, Francisco

AU - Amorim, Joana

AU - Amirkandeh, Melika Besharati

AU - Bodorik, Peter

AU - Cerqueira, Vitor

AU - Gomes, Nuno R.C.

AU - Korus, Jennie

AU - Oliveira, Mariana

AU - Parent, Marianne

AU - Pimentel, João

AU - Reilly, Derek

AU - Sclodnick, Tyler

AU - Grant, Jon

AU - Filgueira, Ramón

AU - Whidden, Christopher

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - advanced analytics

KW - aquaculture

KW - data visualization

KW - machine learning

KW - sustainability

UR - http://www.scopus.com/inward/record.url?scp=85212402794&partnerID=8YFLogxK

U2 - 10.1109/OCEANS55160.2024.10754571

DO - 10.1109/OCEANS55160.2024.10754571

M3 - Conference contribution

AN - SCOPUS:85212402794

T3 - Oceans Conference Record (IEEE)

BT - OCEANS 2024 - Halifax, OCEANS 2024

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - OCEANS 2024 - Halifax, OCEANS 2024

Y2 - 23 September 2024 through 26 September 2024

ER -

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

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Cite this