Optimizing Waste Management: Integrated Pollution Detection and Systematic Reporting for Sustainable Disposal

Effective waste management and pollution control are paramount for sustainable environmental stewardship. This study presents a comprehensive approach leveraging cutting-edge technologies such as YOLO object recognition, Faster R-CNN, and SVM-based AQI computation. The system integrates YOLOv7 for real-time rubbish classification, facilitating swift and precise trash sorting and disposal decisions. Additionally, it employs Faster R-CNN for fog density analysis, enabling thorough estimations of air pollution levels. To calculate the Air Quality Index (AQI), support vector machine (SVM) algorithms combine various air quality indicators, ensuring thorough and accurate assessments. Enhanced trash classification capabilities enable more accurate evaluation of the environmental impacts of waste management systems. By integrating fog density monitoring with AQI calculation, the approach offers comprehensive trend analysis of pollution, aiding in the formulation of data-driven environmental policies and effective pollution management measures. Furthermore, the system evaluates soil quality, complementing its waste management and pollution control functions. Despite its advancements, the system faces challenges in real-world implementation, including data acquisition, model training, and system integration. Addressing these challenges is critical for realizing the full potential of integrated waste management and pollution control systems.