AGRICULTURE & CLIMATE

Satellite-Informed Crop Yield Prediction System

Combined Sentinel-2 satellite imagery, soil IoT sensors, and meteorological models into a Temporal Convolutional Network predicting harvest yields with 91% accuracy.

91% Prediction Accuracy3,000+ Farmers28% Less Fertiliser
Key Takeaways
- Weekly satellite NDVI time-series analysis and in-field IoT soil data provided field-level yield forecasts 60 days before harvest.
- Precision fertilizer recommendations reduced synthetic nitrogen application by 28% while increasing average crop yield by 12%.
- Lightweight offline-first mobile app delivered actionable agronomy advice to 3,000+ smallholder farmers.

The Challenge

An agricultural cooperative of 3,000+ smallholder farmers lacked empirical data to guide fertilizer and irrigation schedules. Over-application of nitrogen damaged soil microbiomes and increased input costs, while unspotted nutrient deficiencies reduced seasonal yields by up to 30%.

Architecture & Technical Approach

  • Multi-Source Data Ingestion: Pulls 10m-resolution Sentinel-2 multispectral imagery (NDVI, EVI) and ERA5 meteorological forecasts.
  • Temporal Convolutional Network (TCN): Model trained on 5 years of historical harvest records predicts crop yields and flags growth anomalies.
  • Mobile Agronomy Client: React Native application provides localized offline agronomic advice and optimal harvesting windows.

Quantitative Benchmarks & Results

Agronomic ParameterIntuition BaselineSatellite AI GuidanceField Improvement
Pre-Harvest Yield AccuracyN/A (Unmeasured)91.2% (±8% Margin)High Predictability
Nitrogen Fertilizer Application100% Baseline72% Baseline28.0% Fertilizer Reduction
Average Field Harvest Yield100% Baseline112% Baseline+12.0% Yield Growth
Input Cost Savings per Hectare$0$85 / HectareSignificant Farmer Margin

Production Reliability & Lessons Learned

Calibrating satellite spectral indices against physical ground-truth soil moisture sensors proved necessary to prevent false drought alerts during cloudy monsoon weeks.