Smart Waste Management: VGG16-Based Classification System for Organic and Recyclable Materials

In today’s world, waste management is a major concern, and efficient trash classification and sorting are becoming more and more necessary. This work uses the VGG16 deep learning architecture to offer a novel garbage classification method that focuses on organic and recyclable materials. Convolutional neural networks like VGG16 are well-known for their superior performance in image classification applications. The goal of this project is to create an accurate and effective automated garbage sorting system based on visual analysis of waste photos by utilising the VGG16 model. The process entails using a dataset of photos of different waste items to train the VGG16 model, with a focus on organic and recyclable material differentiation. The collection has been carefully selected to contain a variety of garbage products that are frequently seen in actual waste management situations. The VGG16 model is extensively trained and fine-tuned, enabling the system to identify important visual cues linked to the categories of organic and recyclable garbage. Evaluating the efficacy of the suggested trash classification system involves employing metrics like F1score, recall, accuracy, and precision. Furthermore, testing on unknown datasets and cross-validation are used to evaluate the model’s robustness and generalization abilities. The outcomes show how well the VGG16-based method works to precisely identify and classify recyclable and organic waste materials with high recall rates.