An Intelligent Solid Waste Classification and Monitoring Alert System using Deep Learning

Efficient waste control is critical for India's smart cities. This study uses Convolutional Neural network (CNN) technology to classify and reveal waste, integrating sensors, Internet of Things (IoT) gadgets, and system studying to promote sustainability. Also targets to deal with inefficient waste management by using CNN technology for effective garbage classification and tracking. The device integrates sensors, IoT devices, and machine learning algorithms to sort and track waste, promoting sustainable practices and environmental conservation. The proposed system uses an ArduinoMKR1000 microcontroller, ultrasonic sensors, and jumper wires to monitor garbage bin levels and classify waste into biodegradable and non-biodegradable classes the usage of CNN algorithms. The system employs the DenseNet121 model for image type and makes use of ThingSpeak for real-time tracking and notifications. The experimental consequences display an accuracy of 95% for the DenseNet121 version, outperforming traditional CNN and ResNet-50 architectures. The proposed device's overall performance surpasses that of different models because of its dense connectivity and efficient feature map creation. Real-time monitoring and data analytics provide precious insights for waste reduction and sustainability tasks, contributing to cleaner, greener, and more sustainable environments for future generations.