Published Paper Details:

Recycling is already a significant work for all countries. Among the work needed for recycling, garbage classification is the most fundamental step to enable cost efficient recycling. Segregation of garbage is also a primary concern in many nations across the world. Even though we are in the modern era, many people still do not know how to distinguish between organic and recyclable waste. It is because of this that the world is facing a major crisis of waste disposal. Population growth and the acceleration of urbanization have led to a sharp increase in municipal solid waste production, and researchers have sought to use advanced technology to solve this problem. Machine learning (ML) algorithms are good at modelling complex nonlinear processes and have been gradually adopted to promote solid waste management and help of the sustainable development of the environment in the past few years. In this paper, we attempt to classify the garbage waste object in images and classify it into seven categories that are cardboard, paper, glass, metal, plastic (mainly domestic waste), e-waste and medical waste. The proposed approaches are simulated and evaluated based on image processing and machine learning. The models we used include multi-kernel support vector machines (SVM) and random forest (RF). The model works in 80-20% training and testing pattern that provides the 86.01% of accuracy for the testing.