We're excited to participate in Hacktoberfest 2025! Help us develop cutting-edge agricultural detection technology that combines machine learning, computer vision, and geospatial analysis.

View our Hacktoberfest README for detailed information on how to contribute.

This is a comprehensive Real USA Agricultural Detection System using our enhanced Adaptive Fusion algorithm with the original architecture pipeline:

Preprocessing → MaskRCNN → RR RT FER → Adaptive Fusion → Post-processing

🏆 State-of-the-Art Performance: 18.7x speedup with 4.98% IoU improvement over CPU implementations

🌍 Large-Scale Validation: Tested across 8 US states with 130M+ building footprints

🚀 Live Demo Available: https://fusing-brains-boundaries.streamlit.app

🤖 Complete Automation: End-to-end pipeline with real-time visualization

Features:

• 🤖 11-Stage Automated Processing with real-time visualization
• 🔄 Adaptive Fusion Technology combining multiple detection methods
• 🔍 High-Precision Results validated across diverse geographical regions
• 📊 Interactive Dashboards for result exploration
• 📱 Responsive Design for mobile and desktop

• Advanced Detection Algorithm: Combines MaskRCNN, RTFNet, and custom YOLO variants
• Optimized Performance: CUDA acceleration with TensorRT optimization
• Scalable Architecture: Containerized with Docker for easy deployment
• API-First Design: REST API for seamless integration with other systems
• Extensive Validation: Tested on 130M+ agricultural footprints across 8 US states

# Clone the repository
git clone https://github.com/vibhorjoshi/Fusing-Brains-and-Boundaries.git
cd Fusing-Brains-and-Boundaries

# Set up environment
python -m venv env
source env/bin/activate  # On Windows: env\Scripts\activate
pip install -r requirements.txt

# Run the demo
python demo_citywise_live.py

# Launch the Streamlit dashboard
streamlit run streamlit_app.py

• Complete Documentation
• API Reference
• Development Guide
• Contribution Guidelines

We welcome contributions! Please see our Contributing Guidelines for details.

We're participating in Hacktoberfest 2025! Check out our Hacktoberfest README for how to get involved.

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