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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 firstdoi:10.3390/nitrogen3020025 fatcat:jeiobbbhxnahtnp77kjbvst2zi