Open AccessDiscrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning
Author(s) -
Yi Chen,
Jun Bin,
Congming Zou,
Mengjiao Ding
Publication year - 2021
Publication title -
journal of analytical methods in chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.407
H-Index - 25
eISSN - 2090-8865
pISSN - 2090-8873
DOI - 10.1155/2021/9912589
Subject(s) - artificial intelligence , maturity (psychological) , linear discriminant analysis , convolutional neural network , pattern recognition (psychology) , mathematics , support vector machine , deep learning , machine learning , artificial neural network , computer science , psychology , developmental psychology
The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches—K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)—were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.