Sustainable Waste Management with AI: Waste Classification Using Deep Learning and IoT-Based Analysis of CH4 Production

The growing worldwide waste problem necessitates creative and long-term approaches to efficient waste disposal. This work introduces a novel method for predicting and classifying decomposable and non-decomposable waste by combining the Internet of Things (IoT) with artificial intelligence (AI), more especially Convolutional Neural Networks (CNN). The proposed system uses a CNN model trained on large datasets to identify organic and inorganic waste items accurately. Moreover, the Blynk platform transfers classification labels to a cloud-based infrastructure for real-time monitoring and analysis via Internet of Things technology. In order to track the presence of methane (CH4) and its concentration in waste bins over the course of ten days, the research also integrates gas and ultrasonic sensors. This all-encompassing strategy attempts to minimize environmental impact, maximize resource utilization, and offer insightful information for sustainable waste management practices. The trained CNN model demonstrated excellent performance in identifying and classifying waste, with an accuracy rate of 96%. The system contributes to a responsive waste management framework and possible biogas production study by enabling real-time monitoring and analysis through the use of cloud-based infrastructure and the Blynk platform.