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ReLeaf: A MobileNetV2-Based Mobile Application for Real-Time Waste Classification with LLM-Assisted Recycling Guidance

Fatimah H. Alyami Nadeen N. Abduljabbar Ghadi T. Alzahrani Dana B. Alakeel Amal S. Almirsal Atheer S. Algherairy
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ReLeaf: A MobileNetV2-based Mobile Application for Real-Time Waste Classification with LLM-Assisted Recycling Guidance

by Fatimah H. Alyami, Nadeen N. Abduljabbar, Ghadi T. Alzahrani, Dana B. Alakeel, Amal S. Almirsal, Atheer S. Algherairy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 116
Year of Publication: 2026
Authors: Fatimah H. Alyami, Nadeen N. Abduljabbar, Ghadi T. Alzahrani, Dana B. Alakeel, Amal S. Almirsal, Atheer S. Algherairy
10.5120/ijcaac10005da779

Fatimah H. Alyami, Nadeen N. Abduljabbar, Ghadi T. Alzahrani, Dana B. Alakeel, Amal S. Almirsal, Atheer S. Algherairy . ReLeaf: A MobileNetV2-based Mobile Application for Real-Time Waste Classification with LLM-Assisted Recycling Guidance. International Journal of Computer Applications. 187, 116 ( Jun 2026), 7-17. DOI=10.5120/ijcaac10005da779

@article{ 10.5120/ijcaac10005da779,
author = { Fatimah H. Alyami, Nadeen N. Abduljabbar, Ghadi T. Alzahrani, Dana B. Alakeel, Amal S. Almirsal, Atheer S. Algherairy },
title = { ReLeaf: A MobileNetV2-based Mobile Application for Real-Time Waste Classification with LLM-Assisted Recycling Guidance },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2026 },
volume = { 187 },
number = { 116 },
month = { Jun },
year = { 2026 },
issn = { 0975-8887 },
pages = { 7-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number116/releaf-a-mobilenetv2-based-mobile-application-for-real-time-waste-classification-with-llm-assisted-recycling-guidance/ },
doi = { 10.5120/ijcaac10005da779 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-06-25T12:52:25+05:30
%A Fatimah H. Alyami
%A Nadeen N. Abduljabbar
%A Ghadi T. Alzahrani
%A Dana B. Alakeel
%A Amal S. Almirsal
%A Atheer S. Algherairy
%T ReLeaf: A MobileNetV2-based Mobile Application for Real-Time Waste Classification with LLM-Assisted Recycling Guidance
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 116
%P 7-17
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper introduces ReLeaf, an intelligent waste classification system that integrates deep learning and mobile application technologies to promote environmentally responsible waste disposal. The approach employs the MobileNetV2 architecture with transfer learning and fine-tuning to classify waste into six categories: cardboard, glass, metal, paper, plastic, and trash. Various data augmentation techniques were systematically evaluated to enhance model generalization and mitigate overfitting. The model was trained on a combination of public datasets and achieved a test accuracy of 93.96%. The trained model was deployed within a Flutterbased mobile application using TensorFlow Lite, enabling realtime waste recognition on mobile devices. Additionally, a cloudbased large language model (GPT-4.1-mini) was incorporated to provide recycling guidance and user assistance through natural language interaction. Experimental results indicate that the proposed system offers an efficient, practical solution for intelligent waste classification and recycling support in real-world settings.

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Index Terms

Computer Science
Information Sciences

Keywords

Waste Classification Deep Learning MobileNetV2 Transfer Learning Data Augmentation TensorFlow Lite Recycling Smart Waste Management Large Language Model

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