The application of machine learning to agriculture is currently experiencing a “surge of interest” from the academic community as well as practitioners from industry. This increased attention has produced a number of differing approaches that use varying machine learning frameworks. It is arguable that Bayesian Networks are particularly suited to agricultural research due to their ability to reason with incomplete information and incorporate new information. Bayesian Networks are currently underrepresented in the machine learning applied to agriculture research literature, and to date there are no survey papers that currently centralize the state of the art. The aim of this paper is rectify the lack of a survey paper in this area by providing a self-contained resource that will: centralize the current state of the art, document the historical progression of Bayesian Networks in agriculture and indicate possible future lines of research as well as providing an introduction to Bayesian Networks for researchers who are new to the area.