SmartCycle: AI-Powered Waste Management ♻️

Table of Contents

• SmartCycle: AI-Powered Waste Management ♻️
  • Table of Contents
  • 💡 Inspiration
  • 🌍 What it does
  • ⚙️ How we built it
  • 🚀 Getting Started
    • Prerequisites
    • Installation
    • Running the Application
  • 🛠️ Technologies Used
  • Challenges we ran into
  • Accomplishments that we're proud of
  • What we learned
  • What's next for SmartCycle

💡 Inspiration

Waste management is a growing global concern, with increasing landfill burden and environmental pollution. Traditional waste collection methods are often inefficient, leading to overflowing bins and missed collections. We were inspired to leverage the power of AI and IoT to create a smarter, more sustainable solution for waste management, promoting better recycling habits and optimized resource allocation.

🌍 What it does

SmartCycle is an intelligent waste management system that aims to revolutionize how we handle waste. It features:

• AI-powered Waste Recognition: Utilizes computer vision to identify and sort different types of waste (e.g., plastic, paper, organic, metal).
• Smart Bin Level Monitoring: Integrates with IoT sensors to monitor waste levels in bins in real-time, preventing overflow.
• Optimized Collection Routes: Generates efficient collection routes for waste management vehicles based on bin fill levels, reducing fuel consumption and operational costs.
• User Engagement Platform: A mobile/web application for citizens to report overflowing bins, view recycling guidelines, and track their environmental impact.
• Data Analytics Dashboard: Provides waste management authorities with insights into waste generation patterns, recycling rates, and operational efficiency.

⚙️ How we built it

SmartCycle was built using a combination of hardware and software components:

• Hardware (Prototype):
  • Raspberry Pi / Arduino for sensor integration (ultrasonic sensors for level detection, camera module for image capture).
  • Basic servo motors for potential sorting mechanisms (future integration).
• Software:
  • Backend: Python with Flask/Django for API development, data processing, and integration with databases.
  • AI/ML: TensorFlow/PyTorch for training the waste classification model (using pre-trained models like MobileNet or custom CNNs).
  • Database: PostgreSQL/MongoDB for storing sensor data, user information, and waste analytics.
  • Frontend (Web): React.js / Vue.js for the administrative dashboard and user portal.
  • Frontend (Mobile - conceptual): React Native / Flutter for the mobile application.
  • IoT Platform: MQTT for communication between IoT devices and the backend.

🚀 Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

• Python 3.8+
• Node.js (for frontend)
• Docker (recommended for easy setup)
• An IDE like VS Code

Installation

1. Clone the repo:

   git clone [https://github.com/TechTusker/smartcycle.git](https://github.com/your-username/smartcycle.git)
   cd smartcycle

2. Backend Setup:

   cd backend
   pip install -r requirements.txt
   # Set up your environment variables (e.g., database connection string)
   # cp .env.example .env
   python manage.py migrate # if using Django

3. Frontend Setup:

   cd frontend
   npm install # or yarn install

4. AI Model Setup:

   • Download the pre-trained model weights (if applicable, instructions will be provided in a separate models/README.md file).
   • If retraining, ensure you have the necessary datasets.

Running the Application

1. Start the Backend Server:

   cd backend
   python app.py # or python manage.py runserver if Django

2. Start the Frontend Development Server:

   cd frontend
   npm start # or yarn start

3. IoT Device Emulation (for testing):

   • You can use a script or a tool like mosquitto_pub to simulate sensor data publishing to your MQTT broker.

🛠️ Technologies Used

• Python
• Flask / Django
• TensorFlow / PyTorch
• OpenCV
• PostgreSQL / MongoDB
• React.js / Vue.js
• Raspberry Pi / Arduino
• MQTT
• Docker
• Git & GitHub

Challenges we ran into

• Dataset Collection and Annotation: Obtaining a diverse and well-labeled dataset for waste classification was challenging. We relied on publicly available datasets and augmented them.
• Real-time Inference on Edge Devices: Optimizing the AI model for efficient inference on resource-constrained devices like Raspberry Pi required careful model selection and quantization techniques.
• Integration of Hardware and Software: Ensuring seamless communication and data flow between the IoT sensors, the backend, and the frontend presented integration hurdles.
• Route Optimization Complexity: Implementing an efficient route optimization algorithm considering multiple dynamic factors (bin levels, traffic) was mathematically complex.

Accomplishments that we're proud of

• Successfully built a working prototype of the AI-powered waste recognition system.
• Integrated IoT sensors to provide real-time bin level data.
• Developed a preliminary route optimization algorithm.
• Created a user-friendly interface for reporting and monitoring.
• Worked collaboratively as a team to overcome technical challenges within a limited timeframe.

What we learned

• Deepened our understanding of computer vision and its applications in real-world scenarios.
• Gained experience in IoT device programming and data communication protocols.
• Improved our skills in backend development, API design, and database management.
• Learned the importance of modular design and scalable architecture in hackathon projects.
• Enhanced our teamwork and problem-solving abilities under pressure.

What's next for SmartCycle

• Further Model Training: Expand the waste classification model to include more categories and improve accuracy.
• Advanced Route Optimization: Integrate real-time traffic data and predictive analytics for even more efficient routing.
• Gamification and Incentives: Introduce features to encourage better recycling habits among citizens.
• Hardware Miniaturization: Develop a more compact and cost-effective smart bin prototype.
• Pilot Program: Collaborate with local municipalities for a pilot deployment and gather real-world feedback.
• Scalability: Design the system to handle a large number of smart bins and users.

🛣️ Road Ahead (Future Features)

• Predictive Maintenance: Implement machine learning models to predict potential sensor failures or mechanical issues in smart bins, enabling proactive maintenance.
• Carbon Footprint Tracking: Integrate with environmental APIs to calculate and display the real-time carbon footprint reduction achieved through optimized waste collection and increased recycling.
• Integration with Smart City Platforms: Explore partnerships and API integrations with existing smart city infrastructure for broader impact and data sharing.
• AR-powered Recycling Guides: Develop an augmented reality feature in the mobile app that overlays recycling instructions onto items when scanned with a phone camera.
• Decentralized Data Management (Blockchain): Investigate using blockchain for secure and transparent recording of waste data, ensuring data integrity and accountability.

🙏 Acknowledgements

We'd like to extend our gratitude to:

• The organizers and mentors for their invaluable support and guidance.
• The open-source community for providing incredible tools and libraries that made this project possible.
• Our families and friends for their continuous encouragement.