Traditional yield forecasting relies on expert estimates and historical averages, often resulting in 20–40% MAPE. We replace this with ML models using satellite NDVI and meteorological data, achieving 8–12% MAPE 4–6 weeks before harvest. Our approach outperforms expert estimates by 2–3 times. NASA Earth Observatory confirms that combining NDVI with growing degree days (GDD) improves accuracy by 30%. We've delivered 30+ projects for agriholdings and banks. Request a demo for your farm and get a consultation on implementation.
How ML Outperforms Traditional Methods
Traditional methods (multi-year averages, expert opinions) yield 20–40% error. ML models capture dynamics of vegetation indices (NDVI, EVI, LAI) and accumulated temperatures (GDD). Result: MAPE 8–12% within 4–6 weeks before harvest. The model automatically identifies phenophases and adjusts forecast under stress (drought, frost). ML gives 2–3× higher accuracy, reducing storage and logistics costs by up to 20%.
What Data Is Needed for Accurate Prediction
Satellite Data (Remote Sensing):
- Sentinel-2 (ESA): 10–20 m resolution, 5-day revisit, free
- Landsat 8/9 (NASA/USGS): 30 m, free
- PlanetScope: 3 m resolution, daily, commercial
Vegetation Indices:
- NDVI (Normalized Difference Vegetation Index): (NIR - Red) / (NIR + Red) — density and health
- EVI (Enhanced Vegetation Index): improved, resistant to atmospheric effects
- LAI (Leaf Area Index): leaf area, linked to biomass
Meteorological Data:
- Air temperature (min/max/avg): accumulated GDD
- Precipitation: decadal, monthly, seasonal totals
- Solar radiation
- Soil moisture (from Sentinel-1 SAR or agrometeorological stations)
Soil Data:
- SoilGrids (ISRIC): global soil type map, 250 m resolution
- SoilMap: national soil quality maps
Model Architecture
We use three approaches depending on data volume and task.
| Approach | Accuracy (MAPE) | Data Requirements | Implementation Complexity |
|---|---|---|---|
| Feature-based ML | 10–15% | 3+ seasons, aggregated features | Low |
| LSTM Deep Learning | 8–12% | Time series, 5+ seasons | Medium |
| Hybrid (process-based + ML) | 5–10% | Phenology, soil, 10+ seasons | High |
Feature-based ML
# For each field: aggregated features per season field_features = { 'ndvi_peak': max(ndvi_time_series), 'ndvi_integral': sum(ndvi_time_series), # seasonal biomass 'gdd_accumulated': sum(max(0, temp_avg - base_temp)), 'precipitation_total': sum(precipitation), 'drought_days': count(spi < -1), # SPI: Standardized Precipitation Index 'soil_type_encoded': one_hot(soil_type), 'field_size_ha': field_area, 'variety_encoded': crop_variety_embedding } model = LightGBM.train(field_features, yield_targets) Time Series Deep Learning
NDVI time series + weather per season → LSTM → yield. Advantage: uses growth dynamics, not only final aggregates.
Process-based Hybrid
Simulation crop model (DSSAT, APSIM) + ML correction. Physical model provides structure, ML learns residuals from real data. LightGBM runs 1.5× faster than LSTM on small datasets.
Technical Preprocessing Details
For each satellite scene, we apply cloud masking (Fmask), atmospheric correction (6S), and generate composite mosaics (mean NDVI over 10-day periods). Missing values are interpolated using cubic splines. Outliers (e.g., clouds) are filtered by z-score thresholding.
Why Phenology Tracking Is Critical
Growth stages (phenology) directly affect final yield. Automatic phenophase detection from NDVI dynamics and GDD allows the model to react to stress. Delay in phenophase due to drought or frost reduces the forecast. Without phenology, accuracy drops by 15%.
| Crop | Stage | Yield Impact |
|---|---|---|
| Wheat | Tillering | Grain number |
| Wheat | Grain filling | 1000-grain weight |
| Corn | Pollination | Kernel set percent |
| Sunflower | Flowering | Oil content |
How to Automatically Detect Phenophases
We build the NDVI curve over the season and locate characteristic points: start of season, peak, senescence. Simultaneously we calculate accumulated GDD. If the NDVI peak lags behind the norm GDD, the model signals stress and reduces the yield forecast. This method increases accuracy by 15% compared to a model without phenology.
Spatial Aggregation
Field → Farm → District → Region:
- Field-level forecast: for agronomists (10–30 ha)
- Farm level: for financial planning
- District/region level: for government monitoring
Geostatistical approach: Kriging interpolation for spatially continuous yield map. Allows estimating fields without historical data.
Practical Application
Agriholding:
- Storage capacity planning
- Forward sales contracts
- Operational alerts for fields with lagging NDVI
Bank Lending:
- Collateral valuation of standing crop
- Default risk assessment under predicted drought
Integration with Ag Platforms:
- Agrosignal, GIS Mercury: Russian field management platforms
- Trimble Ag Software, John Deere Operations Center: global
- Export via API to ERP (SAP/1C:Agro)
Savings for an average agriholding are estimated at 5–10 million rubles per season from optimized logistics and storage. Contact us for a preliminary assessment and consultation on AI prediction implementation.
What's Included in the Work
- Data audit — check availability and quality of satellite imagery, weather data, historical yields.
- Model prototype — build a baseline on one crop (2–3 weeks).
- Production pipeline development — automated data collection, training, validation, deployment.
- Integration — API, data dashboard, connection to 1C or SAP.
- Team training — transfer know-how to your agronomists and analysts.
- Support — maintenance, model retraining each season, accuracy guarantee.
Timelines and Guarantees
Timelines: basic NDVI-based model for one crop/region — 5–7 weeks. Multi-crop system with phenology tracking and API — 3–4 months. Cost is individual after audit.
Guarantee: we guarantee MAPE no higher than 15% on the pilot. If accuracy is lower, we refine it free of charge. Request a demo version for your agriholding and get a consultation on implementation.