Case Study — YieldSphere

Yield Forecasting Platform

Predicting Crop Yields With 94% Accuracy 60 Days Before Harvest

$18M better procurement terms through early, accurate yield intelligence

Industry

Agricultural Commodities Trading

Timeline

16 weeks

Team

6 engineers

Tech

Satellite + Python ML + PostgreSQL

The Challenge

A global agricultural commodity trader needed reliable yield forecasts 60 days before harvest across 200 growing regions to optimize procurement strategy and hedging positions. They were relying on USDA public reports (30-day lag, country-level aggregation) and broker estimates — neither reliable enough for significant procurement decisions.

Our Approach

How We Solved It

Multi-Source Remote Sensing Integration

Integrated Sentinel-2 satellite imagery, NDVI time-series data, soil moisture readings, and historical climate data across all 200 regions into a unified geospatial data pipeline updated weekly.

Yield Prediction Model Development

Trained an ensemble model combining satellite-derived vegetation indices, precipitation anomaly models, and historical yield distributions to produce region-specific yield forecasts at the district level rather than country level.

Weather Risk Scenario Modeling

Built a Monte Carlo scenario engine that overlays weather probability distributions onto yield models, producing best/base/worst-case yield ranges with confidence intervals for procurement risk management.

Procurement Decision Support

Delivered forecasts directly into the procurement workflow with recommended forward purchase quantities, hedge ratios, and price scenarios — translating yield forecasts into procurement and trading decisions.

Engineering Process

How We Built It

Geospatial Data Pipeline

Apache Airflow orchestrates weekly satellite imagery downloads, cloud masking, NDVI calculation, and feature extraction — a 6-step pipeline processing 4TB of imagery per region per season.

Transfer Learning for Data-Sparse Regions

For regions with limited historical yield data, we applied transfer learning from data-rich similar growing regions — extending model coverage to 200 regions when direct training data existed for only 85.

Forecast Versioning and Tracking

Every forecast iteration is versioned with its input data snapshot and model version, enabling retrospective accuracy analysis and systematic model improvement with each season's realized yields.

Architecture Decisions

Key Technical Choices

District-Level vs Country-Level Forecasting

Country-level forecasts mask within-country production variation that significantly affects procurement strategy. District-level granularity required 10x more compute but delivered 3x improvement in actionable accuracy.

Ensemble Over Single Model

Ensemble of LSTM time-series, gradient boosting on tabular features, and process-based crop models outperformed any single model by 12% MAPE — the diversity of modeling approaches captures different aspects of yield variability.

Weekly Forecast Cadence

Daily forecasts added computational cost without accuracy improvement — weekly cadence aligned with satellite revisit schedules and the procurement team's decision cadence, which is bi-weekly.

Results

What We Delivered

94%

Forecast Accuracy (60-Day)

200

Growing Regions Covered

$18M

Better Procurement Terms

60 days

Advance Forecast Horizon

Solution Blueprint

How It All Fits Together

Geospatial Data Layer

Sentinel-2 satellite imagery
NDVI time-series pipeline
Weather & climate data feeds

Forecasting Engine

Ensemble yield prediction models
Monte Carlo weather scenarios
Transfer learning for sparse regions

Decision Support Layer

Procurement recommendation engine
Hedge ratio calculation
Regional risk dashboard

Lessons Learned

What We Improved

Ground Truth Collection Is an Ongoing Investment

Forecast accuracy improved significantly when we added a network of 40 agronomist ground-truth reporters in key regions. Remote sensing alone has systematic biases that ground data corrects.

Procurement Teams Need Ranges, Not Point Estimates

A single 94% accuracy number was less useful to procurement than 80th and 95th percentile confidence intervals by region. The risk range is the input to the hedging decision, not the mean.

Model Accuracy Is Seasonal

Models are most accurate in the 60-40 day window before harvest. Communicating accuracy degradation outside this window prevented procurement teams from over-relying on early-season forecasts.