One of the major components of precision agriculture is the precision fertilization. The principle of precision fertilization is to adjust the fertilizer input according to the properties of soils at each location for the least waste and the highest production of yield. The paper presents a feasible approach for developing high resolution spatial map of soil nutrients based on k-nearest neighbor Convolutional Neural Network (CNN) model. Our CNN-based approach is appropriate for mapping soil nutrients because of its ability to predict soil nutrient value at less computational expense and real-time processing capabilities unlike traditional geostatistical methods. Using the field sampling database from Govt. of India, (Soil Health Card-SHC), soil nutrients (N, P, K and OC) map is generated at field scale (< ha). The nutrients distribution is able to capture the spatial variability with high accuracy. This research is a methodological contribution to precision agriculture and lays the ground for precise application of fertilizers.