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Abstract

Increased plastic waste is becoming a problem that needs new ways of handling recycled waste more efficiently. The current sorting systems based on labour and near-infrared spectroscopy suffer from material variation and the scalability of operation. The given paper addresses the topic of applying artificial intelligence in automatically classifying plastic waste, suggesting a deep learning architecture that incorporates both spectral and image-based analyses. The system uses convolutional neural networks to analyze hyperspectral imagery with the goal of a higher rate of identifying the most typical polymers and combines the limitations of the traditional ones. The aim of industrial feasibility is a modular robotic interface. This work presents the value of AI-based systems in minimizing human involvement in the process of sorting waste and preconditioning sustainable recycling systems.