The future in fishfarms: An ocean of technologies to explore

• Author(s): Pires, A. ; Ferreira, J. ; Klakegg, O.
• Date: 2023
• Persistent ID: http://hdl.handle.net/10071/31759
• Origin: Repositório ISCTE
• Subject(s): Machine learning; Precision fish farming; Data analytics; IoT; Qualidade da água -- Water quality
• Document Type: Conference object
• Language: English

Description

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.