AI USE CASE
Season-Ahead Crop Yield Forecasting
Predict crop yields months in advance using satellite imagery, weather, and soil data.
What it is
Planners gain 3–6 months of forward visibility to optimise procurement, logistics, and pricing decisions. The approach also enables scenario planning against weather shocks, reducing supply chain disruption risk.
Data you need
Multi-year historical crop yield records by parcel, soil composition data, weather station or forecast feeds, and satellite or drone imagery indexed by growing season.
Required systems
- erp
- data warehouse
Why it works
- Secure at least 5 years of parcel-level yield and soil data before model training.
- Integrate real-time satellite imagery updates (e.g. NDVI) throughout the growing season to refine forecasts.
- Embed forecast outputs directly into planning and procurement tools so they drive actual decisions.
- Validate model accuracy against held-out historical seasons before going live with operational decisions.
How this goes wrong
- Insufficient historical yield data at the parcel level makes model training unreliable.
- Weather forecast inputs become stale or low-resolution, degrading prediction accuracy beyond 8 weeks.
- Model is trained on one climate zone but deployed across regions with different soil or precipitation patterns.
- Forecasts are not integrated into ERP or procurement workflows, so planners ignore them in favour of manual estimates.
When NOT to do this
Avoid this investment if the organisation lacks parcel-level historical yield records spanning at least three seasons, as the model will not have enough signal to outperform simple agronomic rules of thumb.
Example vendors
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