Datqorin / Canada
Learning paths for machine learning for crop forecasting
Use these resources to organise a first experiment or review. Begin with the part of the workflow that you need to understand.
Define the forecast
Specify the crop, geographic area, time horizon and unit of yield. Keep a prediction made before harvest separate from a retrospective explanation.
Prepare seasonal records
Align observations to the information available at the forecast date. Inspect missing weather records, field boundaries and changes in measurement methods.
Compare a simple baseline
Evaluate a straightforward historical or statistical reference alongside a more complex model. Separate seasons and related fields carefully to avoid information leakage.
Communicate uncertainty
Report error patterns and the conditions represented in the data. A model estimate should support planning, not be presented as a guaranteed harvest or financial return.