Revolutionizing Waste Management Through Intelligent Garbage Segregation Bin

Efficient waste management stands as a paramount concern in the current environmental landscape. The proper handling, sorting, transportation, and disposal of waste materials are essential to mitigate environmental hazards. The separation of trash into several categories allows for a more accurate evaluation. Conventional manual waste segregation systems necessitate a significant amount of human labor, time, and money. This study proposes a waste management system integrated using advanced technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI), allowing for easy and cost-effective garbage segregation and tracking. The technique is intended to separate garbage into several categories such as glass, plastic, cardboard, paper, metal, and trash. A sample dataset acquired from Kaggle was rigorously processed to design and evaluate the system, which includes operations like as image resizing and rescaling. The DenseNet, is a Deep Learning (DL) model used for accurate garbage recognition. This unique smart segregation bin makes use of the potential of AI and IoT technology. The model is embedded in a controller, allowing it to properly recognize different waste items and trigger the associated actuator for accuracy sorting. A user-friendly Graphical User Interface (GUI) structure makes the entire process visible to the public. This interface enables users to watch the system’s operation in real-time, providing a clear perspective of garbage identification and sorting. By revolutionizing waste management through intelligent garbage segregation, this system enhances the efficiency of waste sorting, contributing to environmental preservation and sustainable waste management practices.