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Pires, A., Ferreira, J. & Klakegg, O. (2023). The future in fishfarms: An ocean of technologies to explore. In  Ajith Abraham, Anu Bajaj, Niketa Gandhi, Ana Maria Madureira, Cengiz Kahraman (Ed.), Innovations in bio-inspired computing and applications: Proceedings of the 13th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2022). (pp. 318-326).: Springer.

A. R. Pires et al.,  "The future in fishfarms: An ocean of technologies to explore", in Innovations in bio-inspired computing and applications: Proc. of the 13th Int. Conf. on Innovations in Bio-Inspired Computing and Applications (IBICA 2022),  Ajith Abraham, Anu Bajaj, Niketa Gandhi, Ana Maria Madureira, Cengiz Kahraman, Ed., Springer, 2023, pp. 318-326

@inproceedings{pires2023_1732205294712,
	author = "Pires, A. and Ferreira, J. and Klakegg, O.",
	title = "The future in fishfarms: An ocean of technologies to explore",
	booktitle = "Innovations in bio-inspired computing and applications: Proceedings of the 13th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2022)",
	year = "2023",
	editor = " Ajith Abraham, Anu Bajaj, Niketa Gandhi, Ana Maria Madureira, Cengiz Kahraman",
	volume = "",
	number = "",
	series = "",
	doi = "10.1007/978-3-031-27499-2_30",
	pages = "318-326",
	publisher = "Springer",
	address = "",
	organization = "",
	url = "https://link.springer.com/chapter/10.1007/978-3-031-27499-2_30#citeas"
}

TY  - CPAPER
TI  - The future in fishfarms: An ocean of technologies to explore
T2  - Innovations in bio-inspired computing and applications: Proceedings of the 13th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2022)
AU  - Pires, A.
AU  - Ferreira, J.
AU  - Klakegg, O.
PY  - 2023
SP  - 318-326
DO  - 10.1007/978-3-031-27499-2_30
UR  - https://link.springer.com/chapter/10.1007/978-3-031-27499-2_30#citeas
AB  - We present the potential application of Machine Learning (ML) to fish farm in a similar approach used in agriculture to control crop growing and predict diseases. The agriculture concept of Precision Agriculture is now applied to fish farm by applying control-engineering principles to fish production; Precision Fish Farming (PFF) aims to improve the farmer's ability to monitor, control, and document biological processes. PFF can help the industry because it takes into consideration the boundary conditions and potentials that are unique to farming operations in the aquatic environment. The proposed solution improves commercial aquaculture and makes it possible to transition to knowledge-based production regime as opposed to experience-based. We apply a data mining approach to identify and evaluate the impact on the growth and mortality of fish in hatcheries. The use of ML techniques, combined with regulation, can increase the productivity and welfare of aquaculture living organisms.
ER  -