GIS, Remote Sensing & Machine Learning in Precision Agriculture

Program Learning Outcomes

1. Build, manage, and map agricultural spatial data in ArcGIS Pro and QGIS using correct coordinate systems (WGS 84, UTM, BTM).
   
   

2. Transform satellite and drone imagery into crop and soil indicators—such as NDVI, NDRE, NDWI, and land surface temperature—using GEE and Pix4D.
   
   

3. Write reproducible Python and R workflows for spatial and agronomic data analysis.
   
   

4. Train, evaluate, and explain machine-learning and deep-learning models for crop mapping, yield prediction, and disease detection.
   
   

5. Translate model outputs into actionable field decisions, including management zones, variable-rate prescriptions, irrigation scheduling, and climate-risk advice.
   
   

6. Communicate results effectively to farmers, extension officers, and policymakers through professional maps, dashboards, and reports.