Recyclitix: Waste Classification with CNN - Mobile Application

Authors

  • Hammam Elkentaoui IIR, Moroccan School of Engineering Sciences, Departement of Computer and Network Engineering,Marrakesh, Morocco https://orcid.org/0009-0001-1148-0519
  • Abdelmounaim Salhi IIR, Moroccan School of Engineering Sciences, Departement of Computer and Network Engineering,Marrakesh, Morocco https://orcid.org/0009-0000-2423-3712
  • Khalid Lamhaddab TIM Laboratory, ENSA, Cadi Ayyad University Marrakesh, Marrakesh, Morocco
  • Younes Zouani Higher School of Technology, Sidi Bennour, Morocco https://orcid.org/0000-0003-3460-4112

Keywords:

Waste Classifier, Deep learning, Mobile application, Convolutional neural networks, Artificial Intelligence (AI)

Abstract

Recylitix is an innovative mobile application that improves waste sorting efficiency through AI-powered image classification and contextual guidance. Leveraging computer vision and deep learning models using TensorFlow Lite, Recyclitix enables users to accurately identify waste types and receive localized recycling recommendations. This intelligent sorting mechanism reduces classification errors, optimizing recycling processes and minimizing the environmental impact of poorly sorted waste. The platform is built on a modern architecture that integrates a Spring Boot backend with a native Android application. Communication between components is facilitated by Retrofit for efficient API interaction. By combining robust machine learning with a user-centric mobile interface, Recylitix bridges the gap between sustainable practices and everyday behavior. It enables individuals, municipalities and waste management players to adopt smarter, more responsible recycling habits.

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Published

2025-09-30

How to Cite

[1]
H. Elkentaoui, A. . Salhi, K. . Lamhaddab, and Y. . Zouani, “Recyclitix: Waste Classification with CNN - Mobile Application”, IJCEDS, vol. 4, no. 3, pp. 35–49, Sep. 2025.

Issue

Vol. 4 No. 3 (2025): Volume 4. Issue 3

Section

Original Software Publication

ARK

https://n2t.net/ark:/32155/IJCEDS.v4i3.93

License

Copyright (c) 2025 Hammam ELKENTAOUI, Salhi Abdelmounaim , Khalid Lamhaddab, Younes Zouani

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