Classification of Organic and Recyclable Waste Using a Deep Learning Approach

In this paper, we propose an automated waste classification system that uses the help of deep learning and image processing methods. The main objective of our proposed system is to streamline the classification of waste into organic and recyclable. To achieve this, we used image processing techniques to improve the image quality of recyclable and organic waste from a publicly available dataset and trained our model using convolutional neural networks (CNN). The model achieved a high validation accuracy of 90.65%. We compared this model with four pre-trained models based on their accuracy and loss. Our proposed model for automating waste classification holds the potential to improve waste management practices and can also reduce the amount of waste that ends up in landfills.