Crop Mapping and Field Boundary Detection

The solution integrates satellite imagery, field observations, data feeds, and GeoAI analytical capabilities to support ARC in maximizing the various crops' yield from limited resources (agricultural land). This is achieved through field boundary detection and crop type detection and classification. The solution provides imagery analysis workflows that leverage Esri geospatial platform capabilities and utilize Azure AI products and services; namely: Azure GeoAI Data Science Virtual Machine (DSVM) and Azure Custom Vision Service.
The agricultural fields in Egypt are commonly distributed with relatively small sizes parcels that usually reduce the reliability of agricultural statistics in surveying cropland. Therefore, the identification of crop types will be implemented by using time series analysis of remote sensing data reflecting crop classification. This will support building an accurate crop inventory under complex landscape conditions based on the spectral characteristics differences of crops. For field boundary detection, a deep learning semantic segmentation model will be trained (using UNet) to detect agriculture field boundaries. This will be accomplished through an end-to-end capability for imagery AI workflows on top of Azure, using the Azure GeoAI DSVM.

The second is developing a scalable and flexible GIS platform that can be adopted across all companies, and finally building AGIBA team capacity (for both the staff to work on and the decision-makers to see what is going on) through providing extensive customized GIS training to different system users to work confidently and productively.