In the near future, the role of aquaculture in human diet is likely to increase due to the rising demand for fish proteins. With a long tradition of rainbow trout (Oncorhynchus mykiss) farming, Italy is one of the main producers of this species in the European Union (EU). The EU is allocating economic resources to foster the sustainable development of the aquaculture sector, aiming to produce more while using less resources. Precision fish farming (PFF) is a promising approach to achieve this goal and its implementation is being facilitated thanks to the reduction of costs of sensors. PFF will likely lead to a new generation of mathematical dynamic models based on sets of control variables and external forcing functions, coping with Big Data and machine learning techniques. In this work, we developed an individual-based dynamic model for the simulation of the fish size distribution and total biomass of a population of rainbow trout within a raceway. At its core, there is a bioenergetic individual model that can simulate weight changes taking into account water temperature and feeding regime. This model was tested against weight observations collected by a non–invasive monitoring system, that was deployed for the first time in a trout farm. The model allows one to estimate the optimal feeding ration based on fish weight and water temperature. The results indicate that current methodologies, based on the estimation of the average weight, lead to slightly overestimate the feed ration: therefore, the model proposed here would allow one to save feed, thus reducing operational costs.
The virtual, digital counterpart of a physical object, referred as digital twin, derives from the Internet of Things (IoT), and involves real-time acquisition and processing of large data sets. A fully implemented system ultimately enables real-time and remote management, as well as the reproduction of real and forecasted scenarios. Under the emerging framework of Precision Fish Farming, which brings control-engineering principles to fish production, we set up digital twin prototypes for land-based finfish farms. The digital twin is aimed at supporting producers in optimizing feeding practices, oxygen supply and fish population management with respect to 1) fish growth performances; 2) fish welfare, and 3) environmental loads. It relies on integrated mathematical models which are fed with data from in-situ sensors and from external sources, and simulate several dynamic processes, allowing the estimation of key parameters describing the ambient environment and the fishes. A conceptual application targeted at rearing cycles of rainbow trout (Oncorhynchus mykiss) in an operational in-land aquafarm in Italy is presented. The digital twin takes into account the disparate levels of automation and control that are found within this farm, and considerations are made on preferential directions for future developments. In spite of its potential, and not only in the aquaculture sector, the development of digital twins is still at its early stage. Furthermore, Precision Fish Farming applications in land-based systems as well as targeted at rainbow trout are novel developments.
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