AI-Powered Food Insecurity Early Warning System
HarvestGuard is an open-source platform that fuses real satellite vegetation data, rainfall anomalies, and WFP food security indicators to generate AI-powered 30-60 day early warnings of crop failure and food insecurity — accessible to NGOs, governments, and humanitarian responders worldwide.
The Problem
828 million people face food insecurity. Smallholder farmers (who produce 70% of the world's food) have zero access to advance crop failure warnings. WFP's HungerMap shows current status; FEWS NET publishes monthly expert reports. No open, accessible platform provides real-time predictive intelligence at country level.
HarvestGuard fills this gap.
How It Works
NASA MODIS NDVI ──┐
CHIRPS Rainfall ──┤──► Risk Engine ──► Claude AI ──► Risk Assessment + Alerts
WFP HungerMap ──┘ ──► Conversational Analysis
Open-Meteo ──┘
- Real satellite data — NASA MODIS vegetation indices updated every 16 days
- Rainfall anomalies — CHIRPS precipitation vs 1981-2010 historical baseline
- Ground truth — WFP HungerMap IPC phases and affected population counts
- AI analysis — Claude generates plain-language risk assessments from real data
- Early warnings — Alerts fire when composite risk scores deteriorate significantly
Data Sources (100% Real, No Synthetic Data)
| Source | Data | Update Frequency |
|---|---|---|
| NASA MODIS MOD13A2 | NDVI/EVI vegetation indices | 16 days |
| CHIRPS | Precipitation anomalies (1981-present) | Monthly |
| WFP HungerMap | IPC phases, affected populations | 6 hours |
| Open-Meteo | Historical & forecast weather | Daily |
Features
- Interactive World Map — 60+ countries color-coded by IPC risk phase
- Real-time NDVI & Rainfall — Satellite vegetation health and precipitation anomaly trends
- AI Risk Assessments — Claude-powered streaming analysis with real data context
- Early Warning Alerts — Automatic detection of deteriorating food security
- Trend Charts — 90-day NDVI + rainfall history per country
- AI Chat — Ask "What's happening in Ethiopia?" and get data-grounded answers
Quick Start
Prerequisites
- Docker & Docker Compose
- Anthropic API key (console.anthropic.com)
1. Clone and configure
git clone https://github.com/rishavsunny12/claude-code-tp cd claude-code-tp cp .env.example .env # Edit .env — set ANTHROPIC_API_KEY
2. Launch
docker compose up --build
3. Seed real data
# Trigger the first data refresh (fetches WFP + weather data)
curl -X POST http://localhost:8000/api/v1/admin/refresh4. Open the app
Navigate to http://localhost:3000
Click any country on the map to see:
- Current IPC phase and affected population
- NDVI and rainfall anomalies vs historical baseline
- 90-day trend chart
- Live-streaming Claude AI risk assessment
API Reference
| Endpoint | Description |
|---|---|
GET /api/v1/regions |
All monitored countries with current risk scores |
GET /api/v1/regions/{iso} |
Full country detail with 90-day history |
GET /api/v1/forecast/{iso} |
Stream AI risk assessment (SSE) |
GET /api/v1/alerts |
Active food security alerts |
POST /api/v1/chat |
Conversational AI query (SSE) |
GET /health |
Health check |
Architecture
harvestguard/
├── backend/ Python FastAPI + SQLAlchemy + APScheduler
│ └── app/
│ ├── services/data/ NASA NDVI, CHIRPS, WFP, Open-Meteo integrations
│ ├── services/analysis/ Risk scoring + alert engine
│ ├── services/ai/ Claude API streaming
│ └── api/v1/ REST endpoints
├── frontend/ React + TypeScript + MapLibre GL + Tailwind
│ └── src/
│ ├── components/map/ Interactive choropleth world map
│ ├── components/sidebar/ Country detail panel
│ └── components/chat/ AI conversation interface
└── docker-compose.yml
Optional: NASA Earthdata (Enhanced NDVI)
For higher-accuracy NDVI from actual MODIS raster data:
- Register free at urs.earthaccess.nasa.gov
- Add
EARTHDATA_USERNAMEandEARTHDATA_PASSWORDto.env
Without credentials, NDVI is estimated from Open-Meteo weather correlates (still useful as a proxy).
Application live screenshots
Contributing
This project is designed to serve humanitarian organizations, governments, and researchers. Contributions welcome — especially:
- Additional data source integrations (ACLED conflict data, FEWS NET)
- Sub-national resolution (admin level 1/2)
- SMS/WhatsApp alert delivery for rural communities
- Multi-language support
License
MIT — free for humanitarian and research use.
Data Attribution
- Vegetation data: NASA/USGS MODIS Land Processes DAAC
- Precipitation: CHIRPS, Climate Hazards Center, UC Santa Barbara
- Food security: World Food Programme HungerMap LIVE
- Weather: Open-Meteo (CC BY 4.0)