A CLOUD PLATFORM FOR PRECISION AQUACULTURE

Precision aquaculture is founded on a set of disparate, interconnected sensors deployed in aquatic environments to monitor, analyse, interpret, and provide decision support for farm operations. This trend parallels developments in agriculture where sensors and other observing technologies lead to enhanced insight into crop production as well as animal health and welfare (Precision Livestock Farming or PLF). The precision aquaculture fundamental approach has been summarised as a series of steps: observe, interpret, decide, and act (Føre et al., 2018). One of the major challenges in Internet of Things (IoT) applications is the effective management of large amounts of time-series data and associated predictive models. Besides the sheer volume of data, substantial complexity arises from the heterogeneity of data sources. In aquaculture farms, these IoT data encompass multiple sensor and data types, with little or no uniformity in terms of communication protocols, or semantic descriptors explaining the data (O’Donncha & Grant, 2020). In this talk we present a service to integrate data from aquaculture sites (with data from pertinent external sources) and interrogate site conditions with a combination of machine learning and mechanistic models.