Interoperability as an Enabler for Digital Twin Based Decision Making in Smart Agriculture

In recent years there has been a significant increase in the use of Smart Farming Technologies (SFTs), which are seen as key enablers in farm management for crop monitoring and reduction of chemical use. SFTs have a wide range of different objectives, such as edge intelligence, smart decision making, and forecasting in the context of fields and crops. The next major challenge in smart agriculture is combining different intelligence components into an all-encompassing system providing deeper insight valuable to farmers, thus transforming it into a truly intelligent system. The Smart Droplets Horizon project aims to solve the problem of interoperability between cloud and edge intelligence components in smart agriculture by making them interoperable through the use of FIWARE Smart Data Models and NGSI-LD, the end goal being efficient and environmentally aware application of pesticides and fertilizers. In the core of this system is a digital twin intelligence, which, in order to give accurate and valuable insight, requires large amounts of data usually coming from heterogeneous sources and agricultural systems. Therefore, resolving interoperability challenges is a key requirement for proper utilization of digital twins in SFTs.