Precision Agriculture and Smart Farming are increasingly important concepts in agriculture. While the first is mainly related to crop production, the latter is more general, which also involves the carbon capture capacity of crop fields (Carbon Farming), as well as optimization of the farming costs taking into account the dynamics of market prices. In this paper we present our recent work in building a web-based decision support system for farmers to help them comply with these trends and requirements. The system is based on the Oskari platform, developed in Finland for the visualization and analysis of geospatial data. Our main focus so far has been in developing tools for Big Data and Deep Learning based modelling which will form the analytical engine of the decision support platform.We first give an overview on the various applications of deep learning in crop production. We also present our recent results on within-field crop yield prediction using a Convolutional Neural Network (CNN) model. The model is based on multispectral data acquired using UAVs during the growth season. The results indicate that both the crop yield and the prediction error have significant within-field variance, emphasizing the importance of developing field-wise modelling tools as a part of a decision support platform for farmers. Finally, we present the general architecture of the overall decision support platform currently under development.