Automated Data Driven Approach To Waste Management

DOI

10.2991/978-94-6463-471-6_138How to use a DOI?

Keywords

computer vision; waste management; trash classification

Abstract

This work follows a systematic approach based on the Software Development Life Cycle (SDLC) to design and develop a garbage collection application using TensorFlow Lite machine learning model in Java within the Android Studio environment. The application addresses waste management challenges by integrating computer vision, machine learning, and geolocation technologies. The project encompasses requirements gathering, where the application's objectives are established, enabling users to classify various types of trash, including plastic, glass, metal, and recyclable plastic. The Android device's camera captures trash images, processed through a TensorFlow Lite machine learning model. Development integrates the trained model into the Android app, offering an intuitive interface for trash classification. The app optimizes TensorFlow Lite for real-time trash detection. Geolocation features enhance waste management, identifying the user's location and guiding them to the nearest recycling trash can through mapping and step-by-step navigation. Real-time monitoring of trash can status enhances efficiency. Rigorous testing ensures reliable trash classification and geolocation. User feedback informs iterative development. Ultimately, the garbage collection application promotes waste segregation, recycling, and environmental sustainability.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kopanati Shankar
AU  - Pentakota Divya Gowri
AU  - Tannieru Hema Vardhan
AU  - Mohammad Hafizunnisa
AU  - Matha Venkat Krishna Mohan
AU  - Nadipineni Venkata Siva Lokesh
PY  - 2024
DA  - 2024/07/30
TI  - Automated Data Driven Approach To Waste Management
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1423
EP  - 1434
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_138
DO  - 10.2991/978-94-6463-471-6_138
ID  - Shankar2024
ER  -