Authors: Assistant Professor P.Premchand Goud, B. soundarya
Abstract: Preserving the environment, promoting sustainable development, and preserving resources all depend on effective waste management. Waste segregation, recycling, and disposal have become more difficult due to the fast growth of municipal solid waste, which has rendered manual classification ineffective and error-prone. To address these issues, this research provides an intelligent waste classification system based on Convolutional Neural Networks (CNNs) for the automated identification of waste elements. Using five predetermined categories—cardboard, glass, metal, paper, and plastic—the suggested approach is able to sort trash photos. Initially, garbage photographs are acquired from a publically accessible dataset and undergo preprocessing processes, including image scaling and augmentation, to increase data consistency and limit the chance of overfitting. The processed pictures are then fed to a CNN architecture consisting of convolutional, activation, pooling, flattening, and fully connected layers for feature extraction and classification. Superbly predicting which trash category an image belongs to, the trained model learns hierarchical visual characteristics automatically from trash photos. The experimental assessment reveals that the suggested CNN-based framework achieves a classification accuracy of 89.88%, with a Precision of 88.12%, Recall of 87.50%, and an F1-score of 87.63%, suggesting its efficacy for automated trash segregation. Furthermore, the trained classification model may be incorporated into an intelligent web-based application to facilitate real-time garbage detection and sustainable waste management methods. The suggested technique helps to enhancing recycling efficiency, eliminating human error, and encouraging environmentally responsible trash disposal using deep learning-based picture categorisation.
