Integrated Smart Waste Management System Based on Artificial Intelligence and IoT

Year 2025, Volume: 1 Issue: 2, 18 - 25, 30.11.2025

Atilla Suncak , Oğuzhan Akkoç , Hüseyin Eren Çeykel , Fatma Zehra Üçgül

Abstract

This study presents an innovative smart waste management system developed through the integrated use of artificial intelligence, IoT and mobile technologies. The system features a multi-layered architecture that combines sensor-based data collection, real-time monitoring via a cloud infrastructure, AI-assisted waste classification and user interaction. Container occupancy rates were measured using an ESP32 microcontroller and ultrasonic sensors, and the data was transferred to the Firebase infrastructure. In the image classification process, the CNN model achieved 94.5% accuracy, while the Gemini model, which requires no additional training, demonstrated superior performance with 97.1% accuracy. The mobile application increased users' recycling awareness, while the web-based panel provided occupancy tracking and route optimization for managers. The results demonstrate that the hybrid CNN-Gemini approach increases waste classification accuracy and system efficiency. This holistic structure is considered a low-cost, scalable and environmentally friendly solution for sustainable smart city applications.

Keywords

Artificial intelligence , Deep learning , IoT sensor , Smart Waste Management , Sustainability

Yapay Zeka ve IoT Tabanlı Entegre Akıllı Atık Yönetim Sistemi

Abstract

Bu çalışmada yapay zekâ, IoT ve mobil teknolojilerin entegre kullanımıyla geliştirilen yenilikçi bir akıllı atık yönetim sistemi sunulmaktadır. Sistem, sensör tabanlı veri toplama, bulut altyapısı üzerinden gerçek zamanlı izleme, yapay zekâ destekli atık sınıflandırması ve kullanıcı etkileşimini birleştiren çok katmanlı bir mimariye sahiptir. Konteyner doluluk oranları bir ESP32 mikrodenetleyici ve ultrasonik sensörler kullanılarak ölçülmüş ve veriler Firebase altyapısına aktarılmıştır. Görüntü sınıflandırma sürecinde CNN modeli %94,5 doğruluk elde ederken, ek eğitim gerektirmeyen Gemini modeli %97,1 doğrulukla üstün bir performans göstermiştir. Mobil uygulama kullanıcıların geri dönüşüm farkındalığını artırırken, web tabanlı panel yöneticiler için doluluk takibi ve rota optimizasyonu sağlamıştır. Sonuçlar, hibrit CNN-Gemini yaklaşımının atık sınıflandırma doğruluğunu ve sistem verimliliğini artırdığını göstermektedir. Bu bütünsel yapı, sürdürülebilir akıllı şehir uygulamaları için düşük maliyetli, ölçeklenebilir ve çevre dostu bir çözüm olarak kabul edilmektedir.

Keywords

Yapay zeka , Derin öğrenme , IoT sensörü , Akıllı Atık Yönetimi , Sürdürülebilirlik

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