README.md

metadata

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.