Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning.

Yi Chen,Jun Bin,Congming Zou,Mengjiao Ding,Jose Vicente Ros Lis

doi:10.1155/2021/9912589

Abstract

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.

Highlights

Harvesting plays an important role in tobacco production. e maturity largely determines the yield, quality, and economic value of tobacco leaves
The potential of NIR spectroscopy coupled with a deep learning method to classify the maturity levels of fresh tobacco leaves was investigated
Erefore, to analyze the impacts of different pretreatment methods on the model construction, the four classical pretreatment methods first derivation, second derivation, standard normal variable transformation (SNV), and multivariate scattering correction (MSC) coupled with Savitzky–Golay smoothing and normalization were used for a comparative analysis

Summary

Introduction

Harvesting plays an important role in tobacco production. e maturity largely determines the yield, quality, and economic value of tobacco leaves. E maturity largely determines the yield, quality, and economic value of tobacco leaves. Fresh tobacco leaves with optimal maturity levels have harmonious internal chemical compositions and high grade and value after flue-curing. As tobacco leaves are collected at intervals as they reach the ripe level, the maturity evaluation for tobacco leaves is manually operated [1, 2]. Grasping the maturity level of tobacco leaves and timely harvesting can ensure quality production as well as better returns [3]. Traditional maturity discrimination and harvesting of tobacco leaves based only on the appearance of tobacco leaves and experience of growers are laborious, inefficient, and quite error-prone. Us, there is an urgent need for a reliable, rapid, and accurate automatically analyzing technique to help growers assessing the maturity levels of tobacco leaves Traditional maturity discrimination and harvesting of tobacco leaves based only on the appearance of tobacco leaves and experience of growers are laborious, inefficient, and quite error-prone. us, there is an urgent need for a reliable, rapid, and accurate automatically analyzing technique to help growers assessing the maturity levels of tobacco leaves

Methods

Results

Conclusion