Intelligent Waste Segregation System Using Convolutional Neural Networks for Deep Learning Applications

Abstract

Efficient waste management is essential for environmental sustainability and reducing landfill burdens. This study proposes an Intelligent Waste Segregation System leveraging Convolutional Neural Networks (CNNs), specifically the VGG-16 model, to automate the classification of waste into recyclable and non-recyclable categories. The purpose of this research is to enhance waste sorting accuracy and efficiency using advanced deep learning techniques. The system employs VGG-16, pre-trained on a large dataset, and fine-tuned with a waste image dataset, enabling high precision in recognizing waste types. The methodology includes dataset preprocessing, model training, and performance evaluation using metrics such as accuracy, precision, and recall. Experimental results demonstrate that the proposed system achieves a classification accuracy of 96%, surpassing existing traditional methods. The implications of this research include improving recycling processes and reducing environmental pollution through accurate waste segregation. This system has practical applications in urban waste management and recycling facilities, providing a scalable solution to global waste challenges. The findings highlight the potential of CNN-based models, particularly VGG-16, in addressing critical environmental issues. In conclusion, the proposed system offers an effective approach to automated waste segregation, paving the way for sustainable waste management practices through deep learning applications.