Leveraging Deep Learning for Sustainable Waste Sorting: Accurate Classification of Organic and Recyclable Materials with Convolutional Neural Networks

This study investigates the application of a Convolutional Neural Network (CNN)-based deep learning model for waste classification, focusing on Organic and Recyclable categories to support the automation of recycling systems. The data was preprocessed using the dataset of 25,077 images obtained from Kaggle, which standardizes dimensions and color channels to prepare for the CNN. The dataset was split into training and testing subsets, to obtain a balanced dataset for robust model evaluation. This model was trained with Adam optimizer, and it was validated by early stopping and checkpointing to avoid overfitting. A confusion matrix was compared alongside key evaluation metrics such as accuracy, precision, recall, and F1 score. Overall accuracy of 94% was achieved by the model, with an F1 score of 0.93 for Organic waste and 0.93 for Recyclable waste. Model's reliability and generalization capability are shown with these results. The feasibility of automated waste classification using deep learning is demonstrated in this research and is a significant contribution in the realm of sustainable waste management. The results demonstrate the viability of real-world application in smart waste management systems and present a scalable and efficient solution to global recycling problems. Future work may extend to additional waste categories and use more advanced preprocessing techniques to further improve model performance.