TerraMind, the AI for sustainable agriculture

TerraMind is an AI-powered environmental intelligence platform that helps farmers, researchers, and policymakers monitor, analyze, and predict land and crop health using satellite imagery, climate data, and machine learning

features:

• Interactive Map Interface: Draw circular Areas of Interest (AOI) on satellite imagery
• Dual Image Analysis: Compare current vs. 12-month-old satellite images
• AI-Powered Analysis:
  • AgroVision: Vegetation health, NDVI/NDWI analysis, crop stress detection
  • EcoGuard: Deforestation and land degradation detection
  • ClimaRisk: Weather risk prediction using climate data
• Predictive Analytics: LSTM/Prophet models for future vegetation conditions
• Automated Insights: AI-generated recommendations and eco-actions
• PDF Reports: Comprehensive reports with maps, analysis, and recommendations

Project Structure

terramind/
├── frontend/                     # react + leaflet + tailwindCSS
│   ├── src/
│   │   ├── components/           # react components
│   │   ├── App.js                # main application
│   │   └── index.js              # entry point
│   ├── public/                   # static assets
│   └── package.json              # dependencies
├── backend/                      # FastAPI + AI/ML modules
│   ├── modules/                  # AI analysis modules
│   │   ├── agrovision.py         # vegetation analysis
│   │   ├── ecoguard.py           # environmental monitoring
│   │   ├── climarisk.py          # climate risk assessment
│   │   ├── insight_engine.py     # recommendations
│   │   ├── satellite_api.py      # satellite imagery
│   │   └── report_generator.py   # PDF reports
│   ├── main.py                   # FastAPI application
│   └── requirements.txt          # python dependencies
├── models/                       # pre-trained AI models
├── data/                         # sample data and cache
├── reports/                      # generated PDF reports
├── docker-compose.yml            # docker configuration
├── setup.sh                      # linux/mac setup script
├── setup.bat                     # windows setup script
└── README.md                     # this file

QuickStart

Option 1: automated setup

Linux/Mac:

chmod +x setup.sh
./setup.sh

Windows:

setup.bat

Option 2: Manual Setup

Prerequisites

• Python 3.8+
• Node.js 16+
• Git

Backend Setup

cd backend
python -m venv venv
source venv/bin/activate  # on windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn main:app --reload

Frontend Setup

cd frontend
npm install
npm start

Environment Configuration

1. Copy backend/env.example to backend/.env
2. Update with your API keys:
   • OPENWEATHER_API_KEY: Get from OpenWeatherMap
   • SENTINEL_HUB_KEY: Get from Sentinel Hub
   • LANDSAT_API_KEY: Get from USGS

Option 3: Docker Setup

docker-compose up

AI Modules

AgroVision Module

• U-Net Segmentation: Vegetation area detection
• NDVI/NDWI Analysis: Vegetation and water content assessment
• Crop Stress Detection: Health monitoring and stress indicators
• Predictive Modeling: Future vegetation condition forecasting

EcoGuard Module

• Land Degradation Detection: Soil health and erosion monitoring
• Deforestation Analysis: Illegal clearing and forest loss detection
• Pollution Assessment: Water and air quality indicators
• Environmental Health Scoring: Overall ecosystem health metrics

ClimaRisk Module

• Drought Risk Assessment: Water stress prediction
• Flood Risk Analysis: Precipitation and runoff modeling
• Heat Stress Monitoring: Temperature impact on crops
• Weather Forecasting: Short and long-term climate predictions

InsightEngine Module

• Contextual Recommendations: AI-generated action items
• Risk Prioritization: Urgent vs. long-term interventions
• Sustainable Practices: Eco-friendly farming suggestions
• Predictive Insights: Future condition recommendations

Tech Stack

Frontend

• React 18: Modern UI framework
• Leaflet.js: Interactive mapping
• TailwindCSS: Utility-first styling
• Chart.js: Data visualization
• Lucide React: Icon library

Backend

• FastAPI: High-performance Python web framework
• PyTorch: Deep learning models
• OpenCV: Computer vision processing
• NumPy/Pandas: Data manipulation
• ReportLab: PDF generation

APIs & Data Sources

• Sentinel Hub: High-resolution satellite imagery
• Landsat: Historical satellite data
• OpenWeatherMap: Climate and weather data
• Custom AI Models: Trained on agricultural datasets

Usage

1. Select Area of Interest: Draw a circular area on the map
2. Run Analysis: Click "Analyze Area" to start AI processing
3. View Results: Explore vegetation, climate, and environmental insights
4. Download Report: Generate comprehensive PDF reports
5. Take Action: Follow AI-generated recommendations

API Endpoints

• GET / - API information
• POST /analyze - Analyze AOI
• GET /analysis/{aoi_id} - Get analysis results
• GET /report/{aoi_id} - Download PDF report
• GET /satellite/imagery - Get satellite imagery
• GET /climate/forecast - Get climate forecast

Docker Deployment

# build and run with docker compose
docker-compose up --build

# or run individual services
docker-compose up backend
docker-compose up frontend

Development

Backend Development

cd backend
source venv/bin/activate
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend Development

cd frontend
npm start

API Documentation

Visit http://localhost:8000/docs for interactive API documentation

screenshots of TerraMind

Impact

TerraMind transforms agricultural monitoring from reactive to predictive by:

• Reducing Waste: Early detection of crop stress and disease
• Improving Yield: Optimized planting and irrigation recommendations
• Protecting Ecosystems: Environmental monitoring and conservation
• Enabling Sustainability: Data-driven farming decisions
• Supporting Policy: Evidence-based environmental management

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License, see the LICENSE file for details

Acknowledgments

• Satellite imagery providers (Sentinel Hub, Landsat)
• Weather data providers (OpenWeatherMap)
• Open source AI/ML libraries (PyTorch, TensorFlow)
• Mapping libraries (Leaflet.js)
• The agricultural and environmental research community

---

TerraMind is my biggest project yet and my first fullstack ai powered app and it was designed on the hopes that i win the local hackathon my uni organized sooo fingers crossed

---

didn't win but i mean we built TerraMind either way so it's still a win for me <3