Application of XGBoost for Fast Identification of Typical Industrial Organic Waste Samples with Near-Infrared Hyperspectral Imaging

Abstract

Waste material identification is an essential part of waste recycling and treatment. Hyperspectral imaging (HSI) enables fast, accurate, nondestructive, and non-invasive identification of waste materials. In this study, HSI-based classification of typical industrial organic waste that cannot be sorted via traditional methods has been explored, namely, leather, paper, plastic, rubber, textile, and wood. The extreme gradient boosting (XGBoost) algorithm, a supervised machine learning algorithm that has never been investigated for waste identification-related fields, was adopted. The results show that XGBoost obtained a higher pixel-wise weighted average F1-score of 82.72% and a faster prediction time of 270 ms for the tested images compared with the commonly used partial least squares-discriminant analysis (77.83% and 444 ms). XGBoost was more effective and efficient in aiding HSI identification and classification of industrial organic waste. The technique can be a significant advancement in the development of an online sorting or identification platform, affording significant labor cost reduction, time savings, and the provision of a stable, accurate, and rapid method for waste intelligent identification.