Green Symphony: A Brief Review of Waste Segregation Techniques

Abstract

Proper waste segregation poses challenges like insufficient awareness, inadequate infrastructure, and limited resources. In this study, we've scrutinized five waste segregation methods employing IoT, Arduino, Deep Learning, Machine Learning, and Artificial Intelligence. The IoT-based system utilizes sensors, and cameras to identify diverse waste types, transmit data to the IoT module, and activate sorting mechanisms like robotic arms [1]. This leads to remote monitoring, and automated waste sorting, fostering efficient recycling. An advanced garbage monitoring system with sensors and an Arduino Uno detects waste types, monitors garbage levels, and issues alerts when bins are full, contributing to waste management efficiency [2]. DL is integrated into the system, categorizing waste images using CNNs, and employing transfer nearest neighbor algorithm predicts waste management alerts based on three sensors, automating notifications to authorities with a 93.3% accuracy rate [3]. AI-driven waste segregation models, particularly for non-biodegradable plastics, reduce waste management costs, achieving over 80% accuracy in sorting plastics. The culmination is the (WSD) model [5], incorporating IoT, Arduino, ML, DL, and AI. Utilizing sensors and imaging devices, this model generates real-time data for efficient monitoring and sorting, featuring a K-nearest neighbor algorithm with 93.3% accuracy and DL precision in material classification. The WSD model provides an automated and eco-friendly solution, reducing manual labor and improving recycling practices for effective waste management. Advanced technology-based techniques like the WSD model offer solutions, emphasizing the importance of choosing technologies aligned with available resources and infrastructure for optimal impact.