Intelligent Waste Management System using Image Classification

Inefficient waste segregation at the source is a major obstacle to recycling and sustainable urban living. Most waste is still sorted manually, which is slow, expensive, and exposes workers to hazardous materials. This paper proposes an intelligent waste management system that uses a convolutional neural network to classify waste items from a camera image into four categories—recyclable, organic, hazardous, and general trash—in real time. The system is designed for integration into a smart bin equipped with a Raspberry Pi, a camera module, and a servo based sorting mechanism. A MobileNetV2 model pre trained on ImageNet was fine tuned on a custom dataset of 8,200 images collected from public waste repositories (TrashNet, TACO) and on site photography. Data augmentation and class weighted loss were used to handle class imbalance. The model achieved 94.2% accuracy on a held out test set with an average inference time of 87 ms on the Raspberry Pi. A field trial with five bins over two weeks in a college campus sorted 2,800 items with an overall classification accuracy of 91.8%, reducing contamination in the recyclable stream by 42%. The system demonstrates that lightweight deep learning can deliver practical, low cost waste segregation that improves recycling rates and reduces manual handling of waste.