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Developed an artificial Intelligence Based Platform through which citizens can post a garbage pickup request and get credits in return. AI is used to detect the genuine- ness of the garbage image. Other feature includes real-time garbage truck tracking, Bio and Non Biodigradable waste identifier, etc.

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Swatchta 2.0

SIH Finale project

Problem Statement

Suggest a solution to help municipal bodies maximize solid waste collection with their given resources.

WebApp Overview

This WebApp features 3 tabs for different users: Admin (Municipal corporation), Employee (Curbside Truck driver), and User (People like us).

Features

Credits: The admin can send credits to users based on the amount of solid waste they give to the curbside truck driver. These credits can later be used to deduct water bills or house tax. Schedule The Pickup: Users can send a pickup request for solid waste, and the curbside truck will come to pick up the waste only after receiving a foolproof photo of it. Live-Tracking: Users can track the curbside truck to ensure they don't miss it. Admin can see all the curbside trucks on a map. Checking Waste: Users can check whether the solid waste is biodegradable or non-biodegradable. Verifying Garbage: The app includes a feature for filtering out spam requests. Prototype Solution The WebApp incentivizes users to dispose of solid waste by rewarding them with credits, which can be converted into a deduction of their water bill.

Challenges and Solutions

Implementation Cost: The Municipal corporation bears the cost of hosting the server, and curbside truck drivers are trained to give credits based on users' waste. User Comfort: The UI is simple, and an Android app is available for global access. Misuse Prevention: The app includes an ML model that determines whether waste is present in a photo. Corruption Prevention: The app establishes one-to-one communication between employees and users, and additional training is provided. User Adoption: Users are incentivized with credits, and the app includes an API that integrates with food delivery and e-commerce apps. An alarm system also alerts users when a curbside truck is nearby.

Conclusion

Although we didn't win in the Smart India Hackathon22 Grand Finale, we learned valuable lessons and plan to continue developing this project. Key takeaways include focusing on practical over theoretical solutions, prioritizing quality over quantity, and being straightforward with our work.

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Developed an artificial Intelligence Based Platform through which citizens can post a garbage pickup request and get credits in return. AI is used to detect the genuine- ness of the garbage image. Other feature includes real-time garbage truck tracking, Bio and Non Biodigradable waste identifier, etc.

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