Big data applications in agriculture: between opportunity and solution

Machine learning and big data solutions are not yet commonly used in agriculture and livestock farming applications. By means of nine case studies, the EU-funded Cybele project aims to connect real-life problems with scalable big data solution providers. This talk presents an assessment of what the drivers for change are for real-life problems within the agricultural domain, for all nine case studies. We investigate what the characteristics for these problems are. We also investigate how technologically mature big data solutions currently are, and how this matureness is expected to change in two years’ time. We follow a mixed-methods approach, combining (a) interviews with nine case study representatives regarding their current situation and what they expect of a future situation, and (b) a survey among various stakeholders and future users of the Cybele platform. In our talk, we give an overview of the perspectives of both case study problem owners and stakeholders. We present the following: (1) what is expected to change in the current decision-making practice when intended big data solutions have been implemented; (2) what do stakeholders see as an opportunity in big data solutions; (3) what is the fit between big data technologies and the case studies’ application areas; (4) what is the technological matureness level of the intended solutions, and (5) what do stakeholders perceive as critical success factors and limiting factors for implementing big data solutions in agriculture. Also, as Cybele progresses, we will include some reflections on the current status of the use cases.