WASTE-RECOGNITION-APP.MD
ANDROID + ML APP
Waste Recognition App
An Android application that helps users identify waste items and improve disposal decisions through ML-backed recognition and guided flows.
Overview
Waste Recognition is a mobile application designed to help users recognize and categorize waste items for better disposal decisions. It combines Android development, Firebase integration, and machine learning functionality in a user-facing app.
Problem
Waste disposal is often inconsistent because users do not have an easy way to identify waste categories quickly. I wanted to build a practical mobile workflow that helps bridge that gap with guided recognition and disposal support.
Role
I worked across the Android application flow, Firebase-backed data handling, and the model-powered recognition experience. My goal was to build something that felt like a usable app, not only a technical model demo.
Implementation
- Built the mobile experience in Java with image capture, recognition flow, history tracking, and guided disposal steps.
- Integrated Firebase-backed data handling to support smoother app behavior and persistent interaction flows.
- Connected the recognition workflow to a TensorFlow Lite-style mobile ML pipeline.
- Improved load-time performance and strengthened reliability through testing and flow optimization.
Outcomes
- Reached 86% accuracy in the recognition workflow.
- Reduced load times by 40%.
- Improved app reliability by 15% through testing and flow refinement.