The global population and its food consumption are growing alarmingly quickly, while climate change effects are simultaneously complicating the challenge of ensuring food security in a sustainable manner (Godfray et al., 2010; Tilman, Balzer, Hill, & Befort, 2011). Data-driven agriculture is one of the main strategies and concepts proposed to increase production efficiently while decreasing its environmental impact (Foley et al., 2011). Data-driven technologies in general are quickly advancing with the development of the Internet of Things (IoT), and may become an important part of the future of farming (Brewster, Roussaki, Kalatzis, Doolin, & Ellis, 2017; Jayaraman, Yavari, Georgakopoulos, Morshed, & Zaslavsky, 2016; Verdouw, 2016; Wolfert, Ge, Verdouw, & Bogaardt, 2017). Smart Farming, also called Agriculture 4.0 or digital farming (CEMA, 2017), is developing beyond the modern concept of precision agriculture, which bases its management practices on spatial measurements largely thanks to Global Positioning System (GPS) signals. Smart farming bases its management tasks also on spatial data but is enhanced with context-awareness and is activated by real-time events, improving the performance of hitherto precision agriculture solutions (Sundmaeker, Verdouw, Wolfert, & Perez Freire, 2016; Wolfert et al., 2017). Additionally, Smart Farming usually incorporates intelligent services for applying and managing Information and Communication Technologies (ICT) in farming, and allows transverse integration throughout the whole agri-food chain in regards to food safety and traceability (Sundmaeker et al., 2016). IoT is therefore a key technology in smart farming since it ensures data flow between sensors and other devices, making it possible to add value to the obtained data by automatic processing, analysis and access, and this leads to more timely and cost-effective production and management effort on farms. Simultaneously, IoT enables the reduction of the inherent environmental impact by real-time reaction to alert events such as weed, pest or disease detection, weather or soil monitoring warnings, which allow for a reduction and adequate use of inputs such as agrochemicals or water. IoT eases documentation and supervision of different activities as well as the traceability of products, improving the environmental surveying and control in farms by the appropriate authorities. The IoT concept was introduced by Kevin Ashton in 1999 in relation to linking Radio-Frequency Identification (RFID) for supply chains to the internet (Ashton, 2009), but has no official definition. It implies, however, the connection of a network of “things” to or through the internet without direct human intervention. “Things” can be any object with sensors and/or actuators that is uniquely addressable, interconnected and accessible through the world-wide computer network, i.e. the Internet. The application of IoT in agriculture is advantageous because of the possibility to monitor and control many different parameters in an interoperable, scalable and open context with an increasing use of heterogeneous automated components (Kamilaris, Gao, Prenafeta-Boldu, & Ali, 2016), in addition to the inevitable requirement for traceability. As a result of IoT, agriculture is becoming data-driven, i.e.