Smart Waste Management System Using Deep Learning

Waste management is a challenging task, particularly when employees move trash by hand. This often occurs in recycling facilities when waste materials are mixed with non-recyclable waste, which reduces recycling possibilities and increases pollution. Our goal is to create an automated waste segregation system that uses deep learning algorithms and servo motors to accurately classify and sort waste materials in real-time. This will promote recycling efficiency, reduce contamination, and minimize human intervention in waste management is proposed using Deep learning which can automatically recognize and sort various forms of garbage and making the recycling process quicker without human intervention. The SegreSmart can automatically identify the type of waste put up into the waste box and categorize the same by using Deep learning, real-time video processing for capturing images, and Arduino-controlled servo motors for automated sorting, all integrated through efficient serial communication techniques. The rotary motor attached with that will help to rotate the arms attached with it, which will be helpful to pour the categorized waste into the corresponding chamber. The SegreSmart was tested with different waste items and it produces 94 % overall accuracy in detecting and categorizing the waste particulars. This allows wastes like as paper, plastic, metal, glass, cardboard and other items to be separated into biodegradable and nonbiodegradable categories and sent for recycling in an effort to protect the environment