Development of a Digital Twin for smart farming: Irrigation management system for water saving

World agriculture faces the challenge of increasing its agricultural production by 50 % from 2012 to 2050, while reducing water consumption, as agriculture accounts for 69 % of all fresh water used on the planet. The use of technologies such as the internet of things, artificial intelligence, digital twins, among others, in the agricultural environment is increasing. Digital twin applied to agriculture is in its early stages of development. A digital twin is one in which data flows automatically and in both directions between a physical object and a virtual object. Withing this context, this paper presents a digital twin of a smart irrigation system in an application scenario. A digital twin for an irrigation system is one in which the physical components of the system, such as its sensors and actuators, are connected with their virtual representations. The system is composed of a FIWARE-based internet of things platform and a discrete event simulation model in Siemens Plant Simulation software. The internet of things platform is used to collect, aggregate and process soil, weather and crop data to calculate daily irrigation prescriptions. The simulation model is used to simulate the behavior of an irrigation system defined in an application scenario. The communication between the platform and the simulation model happens in real-time and is intermediate by an OPC UA server. A application scenario is considered to evaluate the behavior of the system and to evaluate, in future research, different irrigation strategies. The benefits of the systems proposed are twofold. First, evaluate the behavior of the Internet of Things (IoT) platform and an irrigation system before implementing both in the field. Second, enable the evaluation of different irrigation strategies in parallel with current farm practices. Within the system proposed, farmers can evaluate the behavior of the system before implementing in their farms and allowing the system to operate automatically. It is possible to infer that the system can improve farm operations and reduce water usage by allowing farmers to gather soil, weather and crop information and evaluate multiple irrigation strategies.