International Journal for Recent Development in Science and Technology

The increasing global population has led to a significant rise in waste generation, creating a pressing environmental challenge.Effective waste segregation can significantly reduce the volume of waste sent to landfills and enhance the recovery of recyclable materials.This paper presents a waste segregation system that utilizes trained optical and material sensors to classify waste, alongside a mechanical sorting mechanism.The system categorizes waste into four groups: metal cans, plastic bottles, paper, and other materials.A machine learning model was developed using a dataset to identify these waste types, supported by an inductive sensor for material recognition.The mechanical component features servo motors that operate flaps, directing waste into designated containers.Currently, the model achieves an accuracy of 83.54%, which can be enhanced through a web application that allows users to validate images captured by the system, thereby refining the machine learning model.