PREFISHFARM
- DATE: 2024 - 2027
- FUNDING PROGRAM: Public-Private Collaboration Projects of the National Plan for Scientific, Technical, and Innovation Research 2021-2023, co-financed by the European Union.
- PROJECT PARTNERS:
Description
PREFISHFARM (Precision Experimental Fish Farm) is an innovative R&D project designed as a pre-competitive proof of concept to explore the potential of precision aquaculture through the development of an integrated intelligent management and feeding system for marine fish farming. The initiative aims to demonstrate how emerging technologies can transform decision-making processes in aquaculture by combining observation, prediction, decision, and action in a fully automated cycle.
The project is being carried out at an experimental sea bass (Dicentrarchus labrax) farm in Port d’Andratx (Mallorca), with the participation of leading research institutions: RFAP LIMIA & Institut Mediterrani d’Estudis Avançats (IMEDEA, CSIC-UIB) and specialized companies in the sector: Aqüicultura Balear, SAU (Cooke Spain) and FishFarmFeeder (Feeding Systems S.L.).
Through the deployment of sensor networks, underwater computer vision systems, bioenergetic growth models, and artificial intelligence algorithms, PREFISHFARM seeks to advance towards a more efficient, sustainable, and welfare-oriented aquaculture model, tested in real-world farming conditions throughout the grow-out cycle.
PREFISHFARM represents a major step toward the digital transformation of aquaculture, aligning with the principles of the blue economy, energy efficiency, and animal welfare. It is a forward-looking initiative that integrates advanced science and real-world application to shape the future of intelligent fish farming.
Key objectives
Continuous monitoring of fish and environmental conditions using intelligent sensors and AI-powered image analysis tools to estimate critical variables such as fish size, behavior, and uneaten feed.
Development of an individual-based growth model (DEB) capable of predicting fish performance in real time while accounting for individual variability, feeding, and temperature, enabling more precise management decisions.
Implementation of a dynamic decision-making tool, based on agent-based modeling (ABM), to simulate alternative feeding strategies and automatically select the optimal daily ration according to specific management goals (e.g., economic return, environmental impact, fish welfare).
Comparative validation of three feeding strategies: traditional manual feeding, conventional automated feeding systems using short-term AI, and the novel end-to-end system developed within the project. The evaluation focuses on growth, profitability, feed efficiency, and sustainability indicators.
Feed efficiently, produce more, and reduce costs.
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