Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine Algorithm

Abstract

A novel recognition method was put forward to identify the producing areas of the flue-cured tobacco leaves rapidly and non-destructively by using a near-infrared (NIR) spectrometer and a multi-layer-extreme learning machine (ML-ELM) algorithm. In contrast to traditional linear discriminant analysis (LDA) and extreme learning machine (ELM) algorithms, the accuracy, sensitivity and specificity were the highest for the proposed ML-ELM algorithm. The ML-ELM models for different producing areas of Yunnan tobacco leaves had the best generalization ability and prediction results. Besides, the above three algorithms were also identified by using the chemical index data. The experimental results indicated that the NIR spectroscopy technology together with ML-ELM algorithm achieved the best prediction performance both using the NIR spectral data and chemical index data. It indicates that the combination of NIR and ML-ELM can recognize different producing areas of Yunnan tobacco leaves rapidly, accurately, and non-destructively.