Datqorin / Canada
Machine learning for crop forecasting: a practical guide collection
Explore machine learning for crop forecasting through four focused guides for Canadian readers. Start with a defined question, examine the method and keep its limitations visible.
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.