๐ŸŒพ FieldScore AI

Micro-Risk Scoring for Smallholder Farmland

AI-powered field reliability assessment for Agro-lenders using satellite data and weather analytics

Problem & Solution

The Problem

Domain: Financial Inclusion & Agriculture

Smallholder farmers in emerging markets face a critical credit access barrier. Over 60% of loan applications are rejected because:

  • No credit history or collateral available
  • Banks cannot assess farmland productivity or risk
  • Manual field assessments cost $50-200 per farm
  • High default rates (30-40%) discourage lending
  • 2 billion farmers remain excluded from formal credit

Result: Creditworthy farmers denied loans. Lenders face high losses when approving blindly.

Our Solution

FieldScore AI generates objective 0-100 risk scores for individual farm plots in under 10 seconds using:

  • Satellite NDVI data (vegetation health trends)
  • Weather risk analysis (drought/flood indicators)
  • Machine learning scoring models
  • Simple farmer inputs (location, crop, area)

Who Benefits: Microfinance institutions, rural banks, agricultural cooperatives, government lending programs

Impact: Faster decisions, lower default risk, expanded credit access for smallholders

90%
Faster Loan Processing
(weeks โ†’ minutes)
20-30%
Reduction in
Default Risk
$0.10
Cost per Assessment
(vs $50-200)

Our Team

DA

Dovud Asadov

ML Engineer

LinkedIn โ€ข GitHub
BN

Burxon Nurmurodov

Backend Developer

LinkedIn โ€ข
RU

Rustambek Urokov

Data Scientist

LinkedIn โ€ข

Roadmap & Current Stage

Current Stage: PROTOTYPE

Working proof-of-concept with satellite data retrieval, NDVI calculation, and basic scoring logic validated

1

Feature Engineering

Days 1-6

  • NDVI trend analysis pipeline
  • Anomaly detection (z-scores)
  • Rainfall deficit calculation
  • Train gradient boosting model
  • Validate with synthetic data
  • Baseline accuracy: AUC > 0.75
2

API & Deployment

Days 7-12

  • Build FastAPI endpoints
  • GeoJSON validation logic
  • Containerize with Docker
  • Deploy backend to Railway
  • Build Leaflet map frontend
  • Deploy to Netlify
3

Production Ready

Days 13-21

  • User testing with 3-5 MFIs
  • Refine score thresholds
  • Add Redis caching layer
  • Monitoring dashboards
  • API rate limiting
  • Documentation & handoff

How We Plan to Solve It

Data-driven approach combining satellite imagery, weather analytics, and machine learning

Data Sources

  • Sentinel-2: 10m resolution NDVI time series (12 months) via Google Earth Engine API
  • ERA5 / OpenWeatherMap: 30-day historical + 7-day forecast precipitation & temperature
  • SoilGrids (optional): Soil texture and organic carbon data for advanced scoring

Feature Engineering

  • NDVI mean: 12-month baseline productivity
  • NDVI slope: Trend (improving vs declining)
  • 14-day delta: Short-term health indicator
  • Anomaly z-score: Deviation from historical mean
  • Rainfall deficit: 30-day drought stress proxy
  • Coefficient of variation: Stability measure

Technology Stack

  • Backend: FastAPI, GeoPandas, Google Earth Engine API
  • Frontend: HTML/CSS/JavaScript, Leaflet.js for map interface
  • ML Training: scikit-learn, XGBoost/LightGBM, BERT
  • Deployment: Railway (backend), Netlify (frontend)
  • Monitoring: Sentry (errors), structured logging

Development Process

  • Version Control: Git + GitHub
  • CI/CD: Automated deployment on push to main
  • Containerization: Docker multi-stage builds
  • API Design: Single POST /evaluate endpoint
  • Testing: 5-fold cross-validation, unit tests
  • Security: Rate limiting, CORS, API authentication

AI & Machine Learning Implementation

Primary Model: Gradient Boosting (XGBoost or LightGBM) trained on engineered features to output risk scores 0-100. The model learns patterns from historical NDVI trends, weather anomalies, and vegetation stress indicators.

  • Training Data: Synthetic dataset (1,000+ records) combining realistic NDVI patterns with simulated loan outcomes, augmented with public agricultural datasets
  • Model Output: Continuous score representing field reliability, mapped to risk categories (High: 0-30, Medium: 31-60, Low: 61-100)
  • Validation: 5-fold cross-validation with target metrics: AUC > 0.75, precision > 0.70
  • Fallback Logic: Rule-based scoring when satellite data unavailable or confidence low (weighted NDVI thresholds + rainfall deficit)
  • Future Enhancement: Small CNN or LSTM for time-series forecasting of future NDVI trajectory

Score Interpretation: 0-30 (High Risk) โ†’ Declining NDVI or severe drought โ†’ Reject or high interest. 31-60 (Medium) โ†’ Stable but variable โ†’ Standard terms. 61-100 (Low Risk) โ†’ Improving vegetation, adequate rainfall โ†’ Favorable terms.

Key Implementation Steps

  • Data Pipeline:

    Fetch Sentinel-2 NDVI and weather data for input polygon coordinates

  • Feature Computation:

    Calculate all engineered features (trends, anomalies, deficits)

  • Model Inference:

    Run gradient boosting model to generate 0-100 score with confidence level

  • Risk Categorization:

    Map score to loan recommendation (reject, standard, favorable)

  • API Response:

    Return JSON with score, risk category, NDVI chart data, and interpretation

  • Caching:

    Store results for repeated queries (Redis) to reduce API costs

Ready to Transform Agricultural Lending

Bringing data-driven risk assessment to 2 billion underserved farmers worldwide

Learn More About FieldScore AI
๐Ÿค– FieldScore AI Assistant Online
๐Ÿค–

Hi! I'm your FieldScore AI assistant. I can help you with:

  • Understanding farm risk scoring
  • Explaining NDVI and satellite data
  • How to use the demo
  • Technical questions about the model

What would you like to know?