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.
Highlights
Tobacco is a high economic crop in China
The experimental results showed that the combination of NIR spectroscopy and multi-layer-extreme learning machine (ML-extreme learning machine (ELM)) algorithm is a promising tool for identifying the different producing areas of Yunnan tobacco leaves accurately and non-destructively
Our study proposed a novel method using NIR spectroscopy technology together with ML-ELM algorithm to identify the different producing areas of tobacco leaves cultivated in Yunnan province
Summary
Tobacco is a high economic crop in China. The quality of cigarette product is significantly affected by the intrinsic attribute of the tobacco leaf itself. A novel classification method using NIR technology and ML-ELM algorithm was put forward to recognize the producing area of flue-cured tobacco leaves rapidly and non-destructively. The experimental results showed that the combination of NIR spectroscopy and ML-ELM algorithm is a promising tool for identifying the different producing areas of Yunnan tobacco leaves accurately and non-destructively. The second C2F experimental set has 643 samples and the samples were harvested in 2019 It contains 4 different producing areas: Xuanwei, Luxi, Jingdong and Malong cities, Yunnan Province of China. Each ML-ELM hidden layer weights are initialized using extreme learning machine auto encoder (ELM-AE) which performs layer wise unsupervised training. Specificity is the proportion of actual negatives measured that were correct:
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