基于高光谱成像及机器学习的烟叶糖料液施加量判别模型

张建栋; 杨忠泮; 吴恋恋; 徐大勇; 朱萍; 张雯晶; 堵劲松

doi:10.12187/2024.05.010

October 2024

Article Contents

ZHANG Jiandong, YANG Zhongpan, WU Lianlian, et al. Discrimination model of tobacco leaf sucrose solution application levels based on hyperspectral imaging and machine learning[J]. Journal of Light Industry, 2024, 39(5): 86-94. doi: 10.12187/2024.05.010

Citation: ZHANG Jiandong, YANG Zhongpan, WU Lianlian, et al. Discrimination model of tobacco leaf sucrose solution application levels based on hyperspectral imaging and machine learning[J]. Journal of Light Industry, 2024, 39(5): 86-94. doi: 10.12187/2024.05.010 shu

Discrimination model of tobacco leaf sucrose solution application levels based on hyperspectral imaging and machine learning

1. Technology R&D Center, Gansu Tobacco Industrial Co., Ltd., Lanzhou 730050, China;
2. School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
3. Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China

Corresponding author: YANG Zhongpan, 363583280@qq.com
Received Date: 2024-02-05
Accepted Date: 2024-04-19
Available Online: 2024-10-15

Abstract

To address the challenge of non-destructive detection of sucrose solution application in the tobacco leaf processing stage, a discrimination model for sucrose solution application based on hyperspectral imaging and machine learning had been developed. Hyperspectral data of tobacco leaf samples with varying sucrose solution applications were first acquired using a visible-shortwave infrared hyperspectral imaging system and preprocessed with standard normal variate (SNV). Four discrimination models for sucrose solution application were then constructed and validated using full-spectrum data and principal component analysis (PCA) reduced data, in conjunction with support vector machine (SVM), logistic regression (LR), multilayer perceptron (MLP), and random forest (RF). The results showed that SNV preprocessing significantly enhanced the feature concentration of the hyperspectral data. When modeling with full-spectrum data, the models in the shortwave infrared band demonstrated significantly higher prediction accuracy compared to those in the visible light band, with the LR model in the shortwave infrared band achieving the highest accuracy of 98.23%. Compared to full-spectrum data modeling, the prediction accuracy of models using the top 10 principal components from PCA reduced data showed little change in the shortwave infrared band, while the RF model's accuracy in the visible light band improved significantly to 71.43%. In the visible light band, the highest accuracy for PCA-reduced data models corresponded to 217, 55, 47, and 59 principal components, while in the shortwave infrared band, the numbers were 13, 11, 117, and 46, respectively. Overall, LR and RF models exhibited superior predictive perf ormance, with the LR model based on PCA-reduced data in the shortwave infrared band maintaining high accuracy with fewer principal components, demonstrating the capability for rapid, non-destructive, and precise determination of sucrose solution application on tobacco leaves.
- hyperspectral imaging,
- machine learning,
- tobacco leaf processing stage,
- sucrose solution application,
- logistic regression
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Discrimination model of tobacco leaf sucrose solution application levels based on hyperspectral imaging and machine learning

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