Abstract
We are seeing a steady build up in momentum of two trends that will lead to a significant, and necessary, transformation of agriculture in the 21st Century. Firstly, the move to digital with IoT facilitating the use of sensor networks to support precise decision making. Secondly, the move to "ecological intensification"; working with natural processes to lower the carbon footprint of agricultural processes and increase the biodiversity of agricultural units without sacrificing yield. Clearly data mining and machine learning have an important role to play in supporting this transformation. However, given the range of biotic, abiotic and social contexts that need to inform the development of models for decision support in agriculture, we need data mining techniques that support the use of qualitative and unstructured data as well as hard numerical data. In this tutorial we show how Bayesian Networks can be built from a wide range of sources of expert knowledge and data. Whist we use digital agriculture as a key beneficiary of these techniques, this tutorial will also be of interest to all those with an interest in building IoT systems that require combinations of social, technical and environmental understanding.