EcoVentus: AI-Driven UAV Monitoring Platform

Overview

EcoVentus is an advanced AI-powered platform designed to optimize UAV (Unmanned Aerial Vehicle) operations for industries like environmental monitoring, renewable energy, and agriculture. By combining real-time data collection, route planning, and analytics, EcoVentus enables precise, efficient, and sustainable solutions for monitoring and managing key resources.

The platform integrates a web-based interface to control and visualize UAV missions, leveraging artificial intelligence to detect patterns, analyze data, and support informed decision-making.

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Key Features

• Real-Time Monitoring: Visualize UAV flight paths and data collection in real-time.
• Customizable Drone Configuration: Adjust parameters such as altitude, speed, and mission priority.
• AI-Driven Insights: Automatically detect and analyze patterns in collected data.
• Interactive Dashboard:
  • View mission priority scores and flight times.
  • Download Waypoint (WP) files for custom UAV configurations.
  • Visualize routes on a detailed map.
• Scalable Use Cases:
  • Renewable energy: Inspect solar panels and wind turbines for efficiency and damage detection.
  • Agriculture: Monitor crop health and optimize land use.
  • Environmental assessment: Track deforestation, wildlife habitats, and urban development impact.

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Tech Stack

Frontend

• HTML5, CSS3, JavaScript: For creating a responsive and interactive web interface.
• Leaflet.js: To render detailed maps and real-time UAV paths.
• React (Optional): Scalable UI development.

Backend

• Node.js: API development for communication between drones and the platform.
• Python: For AI algorithms and data processing.
• Flask: For hosting machine learning models.

Database

• MongoDB: Storing UAV routes, flight logs, and user information.

AI/ML Frameworks

• TensorFlow / PyTorch: For pattern recognition and anomaly detection.

Cloud Integration

• Google Cloud Vision API: For image classification and analysis.
• AWS or Google Cloud: For scalable hosting and database solutions.

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How to Run the Project

1. Prerequisites

Ensure you have the following installed:

• Node.js (v16+)
• Python (v3.9+)
• MongoDB
• Google Cloud API Key (for Vision API integration)

2. Clone the Repository

1. Clone the repository:

   git clone [https://github.com/AzulRK22/fire-eye-dashboard.git](https://github.com/AzulRK22/queenDrones.git)
   cd queenDrones

2. Set up the frontend:

   cd frontend
   npm install

3. Set up the backend:

   cd ../backend
   python -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   flask db upgrade

4. Start the frontend:

   cd ../frontend
   npm run dev

5. Start the backend:

   flask run
   flask --app app.py --debug run

6. Access the application:

   Open your browser and go to http://localhost:3000 to see the application running.

Screenshots

Home

Monitoring

Reports

Incidents

Virtual Assistant

About us

Use Cases

1. Renewable Energy

• Inspect wind turbines for cracks and defects.
• Monitor solar panel efficiency and cleanliness.

2. Agriculture

• Track irrigation levels and optimize water usage.
• Identify crop diseases and pest infestations.

3. Environmental Monitoring

• Monitor urban development's environmental impact.
• Detect illegal deforestation and changes in wildlife habitats.

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Team Contributions

Multidisciplinary Expertise:

• Electronics Engineering: Designed and integrated UAV hardware components.
• Software Engineering: Developed AI algorithms and the web-based interface.
• Data Analysis: Processed UAV data for actionable insights.
• Environmental Engineering: Ensured eco-friendly operations.
• Market Research: Tailored solutions for specific industries.

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License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Contact

For more details or to collaborate on this project, please contact:
https://www.azulrk.com