Transforming Waste Management: Machine Learning for Efficient Garbage Classification

By integrating cutting-edge technology, the Waste Segregation and Classification System utilizing Machine Learning project offers a creative way to transform waste management procedures. The goal of this project is to create an automated system that can precisely detect and classify a variety of waste materials, enabling effective resource optimization, recycling, and disposal through segregation. The method uses convolutional neural networks (CNNs) and other machine learning approaches to extract visual properties from waste photos, including shape, color, texture, and size. A trained MobileNetV3Large CNN model then makes use of these features to forecast the type or category of garbage. The accuracy obtained reaches 98%. Moreover, this system goes beyond theoretical investigation and incorporates real world applications by utilizing an intuitive website. The website functions as a medium for people to engage with the trash sorting and classification system. In the end, by automating categorization and segregation procedures, lowering errors, improving efficiency, supporting recycling programs, and promoting a more sustainable future, this research advances waste management methods.