Aquaculture System
Aquaculture system research focuses on optimizing fish farming through technological advancements and data-driven approaches. Current efforts concentrate on developing automated systems for feeding, water quality monitoring, and fish health assessment, employing computer vision, IoT sensors, and machine learning algorithms like neural networks, support vector machines, and genetic algorithms for real-time monitoring and predictive modeling. These improvements aim to enhance efficiency, sustainability, and fish welfare, impacting both economic productivity and environmental responsibility within the aquaculture industry.
Papers
Multifidelity digital twin for real-time monitoring of structural dynamics in aquaculture net cages
Eirini Katsidoniotaki, Biao Su, Eleni Kelasidi, Themistoklis P. Sapsis
From operculum and body tail movements to different coupling of physical activity and respiratory frequency in farmed gilthead sea bream and European sea bass. Insights on aquaculture biosensing
Miguel A. Ferrer, Josep A. Calduch-Giner, Moises Díaz, Javier Sosa, Enrique Rosell-Moll, Judith Santana Abril, Graciela Santana Sosa, Tomás Bautista Delgado, Cristina Carmona, Juan Antonio Martos-Sitcha, Enric Cabruja, Juan Manuel Afonso, Aurelio Vega, Manuel Lozano, Juan Antonio Montiel-Nelson, Jaume Pérez-Sánchez
Hybrid Machine Learning techniques in the management of harmful algal blooms impact
Andres Molares-Ulloa, Daniel Rivero, Jesus Gil Ruiz, Enrique Fernandez-Blanco, Luis de-la-Fuente-Valentín
Machine Learning in management of precautionary closures caused by lipophilic biotoxins
Andres Molares-Ulloa, Enrique Fernandez-Blanco, Alejandro Pazos, Daniel Rivero