ML Based Smart Garbage Sorting and Monitoring System

Abstract

Garbage collection, sorting and recycling are major issues in the present-day world. This existing projects sort or recycle after the collection of garbage at one place that requires a lot of human effort and is inefficient. With the urbanization of the world, such a system can prove to be inefficient causing a lot of losses and disease spread. This Project focuses on developing a Machine Learning based Garbage Sorting and Monitoring System to deal with the inefficiencies of manual waste sorting. The project aims to automate waste sorting processes, reduce errors, and enable real-time monitoring in waste management. By leveraging machine learning algorithms and sensor integration, the system targets improved recycling efficiency, reduced contamination, and data-driven waste management decisions. Challenges include algorithm accuracy in diverse conditions and real-time processing on resource-constrained devices. The project has made significant progress in dataset collection, preprocessing, and supervised learning, with upcoming milestones including hardware design, IoT-based monitoring system implementation, mobile application development and system integration. The mobile application will help the management of public areas to monitor garbage levels and take actions accordingly. This innovative approach promises to revolutionize waste management practices for environmental sustainability.