M. Salas, R. Rao, S. Singh, R. Kumar, A. Sarker, A. Singh, J. Yarbrough, A. Mittal, L. Pal
North Carolina State University,
United States
Keywords: waste characterization, waste valorization, AI, computer vision, sustainability
Summary:
Artificial intelligence is redefining how waste materials are analyzed, classified, and transformed into valuable resources. This work presents an integrated AI framework that combines hyperspectral imaging, computer vision, and metadata analytics for intelligent waste characterization. The system captures both spectral and spatial information to identify chemical, structural, and compositional signatures of heterogeneous waste streams, including paper, plastics, and mixed nonrecyclables. Metadata describing source, processing conditions, and chemical attributes enhances contextual understanding and enables correlation across visual and physicochemical dimensions. Together, these data layers create a foundation for automated sorting, process optimization, and valorization pathway predictions. The framework bridges laboratory spectroscopy and large-scale AI deployment, supporting real-time decision-making in material recovery and circular manufacturing. This work demonstrates how linking imaging intelligence with metadata can transform waste from an end-of-life problem into a data-rich feedstock for sustainable resource development.