SmartWaste Classifier

Automate waste sorting with AI. Upload an image, and our LeNet-based CNN accurately classifies it as organic or recyclable. Streamline recycling, minimize contamination, and promote sustainability

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Link: http://127.0.0.1:65535/

EcoSort - AI-Powered Waste Classification for a Sustainable Future

Project Goal: To develop a user-friendly web application, EcoSort, that leverages a Convolutional Neural Network (CNN) based on the LeNet architecture to accurately classify waste items as either recyclable or organic, promoting efficient waste management and environmental sustainability.

Motivation: Improper waste disposal contributes significantly to environmental pollution and resource depletion. EcoSort aims to address this challenge by providing a simple and accessible tool for individuals and communities to correctly sort their waste, leading to increased recycling rates and reduced landfill waste.

Technology Stack:

  • Deep Learning Model: A CNN built using the LeNet architecture, trained on a dataset of images of recyclable and organic waste items.
    • Rationale for LeNet: LeNet is a foundational CNN architecture known for its simplicity and effectiveness in image classification tasks, making it a suitable choice for this project's scope.
  • Backend: Flask (Python) - A lightweight web framework used to create a RESTful API for the image classification model.
  • Frontend: HTML, CSS, JavaScript - Used to build an interactive and intuitive user interface for image uploading and classification results display.
  • Deployment: (Specify your chosen deployment method, e.g., Heroku, AWS, Google Cloud Platform) - To make the application accessible online.

Functionality:

  1. Image Upload: Users can upload images of waste items through the web interface.
  2. Classification: The uploaded image is sent to the Flask backend, which processes it using the trained LeNet CNN model.
  3. Result Display: The application displays the classification result (recyclable or organic) along with a confidence score.
  4. Feedback Mechanism: (Optional) Implement a feedback mechanism for users to report misclassifications, enabling continuous model improvement.

Impact:

  • Increased Recycling Rates: By simplifying waste classification, EcoSort encourages proper recycling practices.
  • Reduced Environmental Impact: Effective waste sorting leads to less landfill waste and reduced pollution.
  • Educational Tool: EcoSort can serve as an educational tool to raise awareness about waste management and sustainability.

Devpost Submission:

This project will be submitted to Devpost to showcase its potential for addressing environmental challenges through innovative technology. The submission will include:

  • Project Description: A detailed overview of the project, including its goals, technology stack, and impact.
  • Demo Video: A short video demonstrating the functionality of the EcoSort application.
  • Code Repository: A link to the project's GitHub repository containing the source code.
  • Technical Documentation: Documentation outlining the project's architecture, implementation details, and instructions for running the application.

Future Enhancements:

  • Expanded Classification Categories: Extend the model to classify more waste categories (e.g., specific types of plastics, glass, metal).
  • Mobile Application: Develop a mobile application for on-the-go waste classification.
  • Integration with Smart Bins: Explore integration with smart bins for automated waste sorting.

EcoSort represents a step towards a more sustainable future by empowering individuals to make informed decisions about waste disposal through the power of AI.

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