Smart Agriculture · Advanced
Digital Soil Mapping & Crop Intelligence
Predict soil properties across landscapes with covariates, machine learning and uncertainty mapping.
Course modules
- 01Soil data, sampling design and the scorpan framework
- 02Environmental covariates from terrain and imagery
- 03Machine-learning soil property prediction
- 04Uncertainty quantification and validation
- 05Soil health, nutrients and fertility mapping
- 06Crop-soil intelligence for recommendations
Learning outcomes
- Build a digital soil map with uncertainty
- Design efficient soil sampling
- Link soil maps to crop and nutrient decisions
Project-based training
Every participant completes a capstone: Crop-soil intelligence for recommendations. Labs use real environmental, agricultural or engineering datasets.
Tools
- R / Python
- QGIS
- Google Earth Engine
- Random Forest
Who it’s for
Soil science, agronomy and GIS researchers
Prerequisites
GIS basics and introductory statistics
