Prototyping waste collection by the implementation of a smart bin using AI techniques

This paper presents a novel approach to smart waste management, leveraging Internet of Things (IoT) and cutting-edge AI algorithms. The approach used endeavors to effectively tackle the persistent challenges in waste management, taking into account critical factors such as efficiency, cost-effectiveness, and environmental sustainability. In a world struggling with the annual generation of an estimated 2.01 billion tons of waste, with a significant amount being handled to the detriment of the environment, the management of waste has evolved into a challenging global issue. Focusing on the context of Mauritius, a nation that generates approximately 1,488 tons of waste daily and largely relies on a single landfill, Mare Chicose, for its disposal, this research embarks on an innovative journey. It combines the capabilities of Artificial Intelligence (AI) through Convolutional Neural Networks (CNN) and regression models, coupled with the Internet of Things (IoT), to craft a waste management system that is not only efficient but also environment friendly and economical. By harnessing the power of data collection and analysis, the system aims to predict waste levels. In doing so, it presents a potential avenue to optimise waste management operations, empower data-driven decision-making, and champion the cause of sustainability and environmental preservation. The paper extensively discusses the current waste management issues, proposed AI-enabled solution, system requirements, architecture, implementation, and evaluation metrics. It offers significant insights that could reshape waste management strategies, not just in Mauritius, but globally.