Modelling Data For A Sustainable Aquaculture

Abstract

Combining aquaculture and Internet of Things (IoT) technologies still poses several challenges. IoT technologies are exploited to enhance productivity in aquaculture sites by maintaining precise operating conditions and to avoid undesirable situations. Currently, human intervention is still needed to make good decisions, dependant on the values of parameters detected by sensors, often captured at one point in time in a day. This is not only a time-consuming task but also an inaccurate one since parameters, such as water quality, evolves continuously and affects the whole aquaculture system. This is mainly caused by the lack of information collection, exchange and process automation between different actors in aquaculture farms. To overcome these problems, technologies such as semantic Web technologies and Artificial Intelligence (AI), are bringing new capabilities to organise data in an inter-operable way to then be processed and used for monitoring and decision-making. In this context, we are proposing in this paper a fully semantic based-reference data model for Integrated Multi-Trophic Aquaculture (IMTA) that involves the collection, processing, and sharing of data both between components and between the platform and external aqua-systems by supporting IoT and data information standards. It is based on analysis of the current data models and covers the core concepts required for aquaculture management and has been validated against several business scenarios.