AI Waste Management System
Inspiration
In urban areas, waste accumulation on streets and in public spaces is a significant issue, leading to environmental pollution, health risks, and unsanitary conditions. Inspired by the need for a cleaner, healthier urban environment, the AI Waste Management System aims to empower citizens and authorities to collaboratively tackle waste management with the help of technology. By making it easy to report, categorize, and collect waste, the system encourages community participation and ensures that waste is managed effectively.
What It Does
The AI Waste Management System allows users to:
1. Report Waste: Users can report waste by taking a photo, which the app scans using AI (powered by Gemini AI) to categorize the type of waste and estimate its carbon footprint.
2. Collect Waste: Any registered user, whether an individual, volunteer, or waste management professional, can check reported waste locations and collect it. This action is updated on the app, marking the waste as "collected."
3. Earn Rewards: Both waste reporters and collectors earn points that can be redeemed for rewards, incentivizing participation.
4. Track Waste in Real-Time: The app uses Google Maps to show waste locations in real-time, making it easy to identify, report, and act on waste hotspots.
How We Built It
1. Frontend: Built using React,Nextjs and Tailwind CSS for a smooth and responsive user experience.
2. Backend: Integrated Firebase for authentication, PostgreSQL for data storage, and points management.
3. AI & Image Processing: Gemini AI for waste identification and categorization, providing insights into waste type and estimated carbon footprint.
4. Mapping & Geolocation: Google Maps API is used to map waste locations, making it easy for users to identify and navigate to waste sites.
5. UI/UX: Radix UI and TailwindCSS ensure a clean, user-friendly interface that is accessible and intuitive.
Challenges We Ran Into
1. Accuracy of AI: Training the AI to accurately categorize waste types in different environments was challenging and required substantial model adjustments.
2. Real-Time Mapping: Integrating Google Maps with real-time updates presented challenges in syncing data without performance lag.
3. Incentive Structure: Designing an effective points and rewards system that maintains user engagement was a strategic challenge, as it needed to be motivating without being too complex.
Accomplishments That We're Proud Of
1. AI-Powered Waste Categorization: Successfully integrating Gemini AI to identify waste types and estimate their carbon impact.
2. Community-Driven Waste Collection: Building an inclusive platform where anyone can contribute to waste collection, thereby fostering a sense of community responsibility.
3. User-Centric UI: Creating a user-friendly interface that is both visually appealing and functional, with an accessible design for diverse user groups.
What We Learned
AI Integration: Integrating AI for real-time image processing requires both technical and strategic planning, especially when dealing with environmental factors in image accuracy.
Community Engagement: Providing tangible rewards (points and redeemable rewards) is a powerful motivator in encouraging users to contribute to public health and safety.
Collaborative Waste Management: By opening the platform to all users, we realized that community-driven waste management can be an efficient approach for urban cleanliness.
What's Next for the AI Waste Management System
1. Enhanced AI Accuracy: Further training for AI to improve accuracy in complex environments, as well as categorizing more types of waste.
2. Expanded Reward System: Partnering with local businesses to offer more rewards, encouraging sustained community participation.
3. Analytics Dashboard: Adding an admin analytics dashboard for urban authorities to analyze waste patterns, frequent locations, and types of waste for better resource allocation.
4. Scalability & New Features: Expanding the app to incorporate recycling tips, scheduled waste pickups, and city-wide waste management reports.