A High Efficient and Cost Effective Waste Sorting Solution using Advanced Sensors and AI

The rapid increase in global waste, including plastic, metal, electronic waste (e-waste), and hazardous materials, necessitates an efficient and automated waste management system. Traditional manual waste sorting is labor-intensive, error-prone, and poses health risks due to exposure to hazardous materials. Existing automated solutions, such as IoT-based waste monitoring and basic sensor-based segregation, suffer from low classification accuracy, high misclassification rates, and limited adaptability to complex waste types. This research addresses these challenges by proposing an AI-driven waste segregation system integrating machine vision, YOLO (You Only Look Once) object detection, and Convolutional Neural Networks (CNNs) for real-time waste classification with improved precision. The system utilizes an ESP32-CAM microcontroller to capture and process images, metal sensors to distinguish ferrous and non-ferrous metals, and gas sensors to detect hazardous gases such as methane (CH4), carbon monoxide (CO), and ammonia (NH3). Motorized actuators then autonomously direct waste into designated bins, reducing human intervention and increasing sorting efficiency. Experimental results demonstrate that the proposed system achieves a classification accuracy of 97-98%, significantly outperforming manual sorting (83-85%). Additionally, misclassification rates were reduced by fivefold, particularly in hazardous waste detection, where errors dropped from 1.5 kg to 0.3 kg per 100 kg of waste processed. The integration of advanced AI models and sensor technologies ensures precise waste categorization, optimizing recycling processes, minimizing landfill waste, and enhancing workplace safety. This scalable and cost-effective solution supports smart city initiatives and sustainable waste management by automating waste segregation with high efficiency, adaptability, and minimal operational costs.