Nitrogen management through a leaf colour chart proved to be a cost-effective strategy for small-scale farmers. Identifying a research gap in the realm of variable rate technology, the integration of leaf colour chart readings into precision fertilizer application machinery became the focus. Consequently, a study was initiated to utilize the leaf colour chart data in creating digital prescription maps for unmanned aerial or ground vehicles. Two experimental rice fields were established and assessed for nitrogen deficiency using the leaf colour chart, paired with GPS coordinates. The collected data underwent processing in Google Earth Pro and ArcMap softwares for spatial interpolation of observed data to generate digital prescription maps through predicted data. The Karl Pearson correlation coefficient and Goodness of Prediction (G) of the predicted values with observed values were 0.93 and 0.85. The statistical analysis also confirms (p and f value) the non-existence of significant differences between observed and predicted data. The developed maps were extracted with set of geopositioning and corresponding prescription categories as input data to the precision machinery. This research highlights the potential application of leaf colour chart data and geographical information system tools in unmanned precision fertilizer application machinery.