This paper proposes an automatic, machine learning methodology for precision agriculture, aiming at learning management zones that allow a more efficient and sustainable use of fertiliser. In particular, the methodology consists of clustering remote sensing data and estimating the impact of decision-making based on the extracted knowledge. A case study is developed on experimental data coming from winter wheat (Triticum aestivum) crops receiving site-specific fertilisation. A first approximation to the data allows measuring the effects of the fertilisation treatments on the yield and quality of the crops. After verifying the significance of such effects, clustering analysis is applied on sensor readings on vegetation and soil electric conductivity in order to automatically learn the best configuration of zones for differentiated treatment. The complete methodology for identifying management zones from vegetation and soil sensing is validated for two experimental sites in Denmark, estimating its potential impact for decision-making on site-specific N fertilisation.