  Digital agriculture is growing in popularity among professionals and brings
together new opportunities along with pervasive use of modern data-driven
technologies. Digital agriculture approaches can be used to replace all
traditional agricultural system at very reasonable costs. It is very effective
in optimising large-scale management of resources, while traditional techniques
cannot even tackle the problem. In this paper, we proposed a dynamic management
zone delineation approach based on Machine Learning clustering algorithms using
crop yield data, elevation and soil texture maps and available NDVI data. Our
proposed dynamic management zone delineation approach is useful for analysing
the spatial variation of yield zones. Delineation of yield regions based on
historical yield data augmented with topography and soil physical properties
helps farmers to economically and sustainably deploy site-specific management
practices identifying persistent issues in a field. The use of frequency maps
is capable of capturing dynamically changing incidental issues within a growing
season. The proposed zone management approach can help farmers/agronomists to
apply variable-rate N fertilisation more effectively by analysing yield
potential and stability zones with satellite-based NDVI monitoring.
