A DEEP LEARNING APPROACH FOR AUTOMATED WASTE TYPE IDENTIFICATION IN SMART WASTE MANAGEMENT SYSTEMS

Abstract

Effective waste management is essential in India to ensure environmental sustainability and safeguard 
public health. With rapid urbanization and population growth, the volume of waste generated has 
surged, overwhelming traditional disposal systems and leading to pollution, ecosystem degradation, 
and health risks. Conventional methods such as manual sorting, rule-based classification, and 
exporting waste have long been used but present significant limitations. Manual sorting is labor
intensive and error-prone, making it unsuitable for large-scale implementation. Rule-based systems 
lack flexibility to adapt to the diverse and dynamic nature of waste, while exporting waste raises 
ethical and environmental concerns due to the risks of mismanagement. To address these challenges, 
this research utilizes a large-scale dataset containing millions of waste item images to train and 
evaluate classification models. The study integrates MobileNetV2 and a lightweight convolutional 
neural network to develop an efficient, automated waste classification system. This system is designed 
for deployment in smart waste bins and recycling centers, aiming to improve sorting accuracy, 
increase recycling efficiency, and minimize environmental harm.