---
title: Future Crop Simulator
emoji: 🌾
colorFrom: green
colorTo: blue
sdk: docker
pinned: false
license: mit
app_port: 7860
---

# Future Crop Simulator 🌾

An advanced agricultural simulation platform that combines vegetation indices, weather forecasts, and soil data to generate future crop growth predictions and optimize crop rotation plans.

## Features

### 🌱 Core Capabilities
- **Multi-season crop growth simulations** with NDVI/EVI time series analysis
- **Weather-based forecasting** with scenario modeling (wet year, dry year, heatwave, low inputs)
- **Crop rotation optimization** using RL-style algorithms
- **Growth stage predictions** along detailed timelines
- **Soil and terrain analysis** with suitability scoring
- **Multi-panel visualization** of historical and forecasted data

### 📊 Simulation Scenarios
- **Baseline**: Standard growing conditions
- **Wet Year**: Increased rainfall scenarios
- **Dry Year**: Drought conditions
- **Heatwave**: Extreme temperature events
- **Low Inputs**: Reduced fertilizer/resource scenarios

### 🎯 What-If Analysis
Run comparative simulations to understand:
- Yield potential under different climate scenarios
- Crop stress risk factors
- Optimal rotation sequences for soil health
- Best crop selections based on local conditions

## How It Works

### Data Pipeline
1. **Historical Data**: NDVI/EVI vegetation indices from satellite imagery (synthetic stubs for demo)
2. **Weather Forecasts**: 30-day forward-looking temperature and rainfall data
3. **Soil & Terrain**: Elevation, slope, texture, organic matter, and drainage profiles
4. **Rotation Planning**: User-defined or optimized multi-season crop sequences

### Model Architecture
- **Temporal Forecaster**: Growth trajectory predictions based on historical patterns
- **RL Optimizer**: MuZero-style rotation planning with simulated rollouts
- **Visualization Engine**: Multi-panel matplotlib-based imagery generation
- **Metrics Engine**: Yield index, stress risk, soil scoring, and health indicators

## Usage

1. **Select your region** from available field IDs
2. **Choose a primary crop** (Maize, Wheat, Rice, Soybean, Cotton)
3. **Set simulation parameters**: start date and number of seasons
4. **Pick a scenario** to model different climate conditions
5. **Define crop rotation** (inline text or upload CSV)
6. **Run simulation** to generate visualizations and recommendations

## Architecture

### Backend Components
- `data_loader.py`: Data ingestion and preprocessing
- `temporal_forecaster.py`: Growth prediction models
- `rotation_optimizer.py`: Crop sequence optimization
- `image_generator.py`: Visualization generation
- `config.py`: System configuration

### Frontend
- Bootstrap 5-based responsive UI
- Multi-panel input forms
- Interactive results dashboard
- Downloadable visualizations

## API Endpoints

- `GET /`: Main simulation interface
- `POST /`: Submit simulation parameters
- `GET /api/health`: Health check endpoint

## Data Sources (Production Ready)

This demo uses synthetic data stubs. In production, connect to:
- **Landsat 8/9 NDVI** via USGS Earth Explorer
- **MODIS NDVI/EVI** via NASA Earthdata
- **Open-Meteo** for weather forecasts
- **Copernicus** for terrain and soil data
- **USDA Ag Data Commons** for agricultural metadata

## Technical Stack

- **Backend**: Python 3.10, Flask 3.0
- **Visualization**: Matplotlib 3.8
- **Data Processing**: NumPy 1.26
- **Deployment**: Docker on Hugging Face Spaces

## MLOps Ready

- Single entry point architecture
- Clear module contracts for model swapping
- Environment-based configuration
- Health check monitoring
- Extensible for FastAPI integration

## Future Enhancements

- Replace synthetic loaders with real satellite API integrations
- Implement actual TFT/Informer temporal models
- Deploy real MuZero RL agent for rotation optimization
- Add GAN/diffusion models for advanced crop imagery
- Multi-objective optimization (yield, profit, risk, sustainability)
- Historical comparison dashboards
- Export functionality for reports

## License

MIT License - Feel free to use and modify for your agricultural AI projects!

## Credits

Built as Feature 3 of an integrated agro-tech platform demonstrating future crop visualization capabilities.