Frost forecast is an important issue in climate research because of its economic impact in several industries. In this study, a graph neural network (GNN) with spatio-temporal architecture is proposed to predict minimum temperatures in an experimental site. The model considers spatial and temporal relations and processes multiple time series simultaneously. Performing predictions of 6, 12, and 24 hrs this model outperforms statistical and non-graph deep learning models.