Artificial Intelligence in Recycling: Waste Management with Convolutional Neural Networks

The rapid growth of urbanization and economic development has led to a significant increase in household waste, highlighting the necessity for developing sustainable waste management processes. Traditional waste sorting methods are based on manual processes, leading to high labor costs and low efficiency. This makes waste management systems less environmentally and economically sustainable, especially in densely populated areas. Furthermore, these methods reduce the efficiency of recycling processes and waste valuable resources by making it difficult to correctly sort waste by type. The time-consuming nature of manual methods and the risk of human error necessitate technological solutions to improve these processes. In this context, artificial intelligence-based technologies play an important role in waste management processes. Artificial intelligence reduces costs and increases efficiency by minimizing human errors through fast and accurate sorting processes. This study employed a publicly accessible dataset containing 22,564 images classified as recyclable and organic waste to train Convolutional Neural Network (CNN) models. The models’ performance was assessed using metrics such as validation accuracy and validation loss. The findings indicate that the Optimized Convolutional Neural Network (OCNN) model achieved superior generalization capacity with a validation accuracy of 90.41%, outperforming the traditional CNN model, which attained 88.26%. This study aims to increase environmental sustainability and improve economic efficiency in waste management by using innovative methods. The proposed approaches are developed to increase the efficiency of waste classification processes, thereby supporting the conservation of natural resources and promoting higher recycling rates.