Eco Track AI

EcoTrack AI uses artificial intelligence to identify waste, recommend proper disposal methods, and help people make sustainable choices to reduce environmental pollution.

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Inspiration

Environmental pollution and improper waste disposal are major challenges in our daily lives. Many people are unsure how to separate recyclable, organic, electronic, and hazardous waste correctly. We wanted to create a simple technology-based solution that helps people understand waste disposal and develop more sustainable habits. This inspired us to build Eco Track AI.

What it does

Eco Track AI is a smart waste management and environmental tracking web application. It helps users organize waste, understand appropriate disposal methods, and monitor their waste management activities.

Key features include:

  • Waste Classification: Helps users identify and organize different categories of waste.
  • Disposal Guide: Provides disposal guidance for organic waste, paper, plastics, glass, metals, electronic waste, and hazardous waste.
  • Waste Tracker: Allows users to record waste items and track their weight and category.
  • Environmental Impact Dashboard: Displays waste statistics and estimated environmental impact.
  • Eco Tips: Provides practical suggestions to reduce waste and adopt environmentally friendly habits.
  • Demo Mode: Uses sample waste records to demonstrate the application's features.

How we built it

We built Eco Track AI as a web application using React, TypeScript, Vite, and Tailwind CSS. We organized the application into reusable components and separate modules for waste categories, eco-friendly tips, sample data, waste calculations, and classification functionality.

We used browser LocalStorage to store waste records locally, allowing users to explore the application without requiring a backend database. The dashboard presents waste statistics and category breakdowns to make the information easy to understand.

Our development process focused on creating a user-friendly interface, organizing waste management features, and demonstrating how technology can support more responsible disposal practices.

Challenges we ran into

One of our main challenges was organizing different waste categories and providing clear disposal guidance for each category. We also needed to make the dashboard easy to understand while displaying useful information about waste quantity and environmental impact.

Another challenge was developing the application within the hackathon timeframe and ensuring that the main features worked together. Using sample records and browser-based storage helped us demonstrate the core workflow during development.

Accomplishments that we're proud of

We are proud to have developed a functional web application that brings waste classification, disposal guidance, waste tracking, and eco-friendly tips together in one place.

We created a dashboard that presents waste data visually and built a demo mode that allows users to explore the application using sample records. We are also proud that our project focuses on a real-world environmental problem and encourages practical steps toward sustainability.

What we learned

Through this project, we learned more about developing web applications with React and TypeScript, organizing code into reusable modules, managing data in browser storage, and designing dashboards that communicate information clearly.

We also learned the importance of breaking a large problem into smaller features, testing the application, and prioritizing essential functionality within a limited development period.

What's next for Eco Track AI

Our future plans include improving image-based waste classification, adding more accurate environmental impact calculations, and providing location-specific disposal guidance for users.

We also hope to integrate reliable waste classification models, expand recycling recommendations, and introduce additional features that help users understand their waste habits and make more sustainable choices.

Built With

  • css-frameworks-&-libraries-react
  • environmental
  • html
  • javascript
  • languages-typescript
  • machine-learning-other-data-visualization
  • npm-databases-/-storage-localstorage-ai-/-machine-learning-artificial-intelligence
  • tailwind-css-tools-google-antigravity
  • vite
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