On-Line Monitoring of the Tobacco Leaf Composition during Flue-Curing by Near-Infrared Spectroscopy and Deep Transfer Learning

Abstract

The yield, quality, and output value of tobacco leaves are strongly affected by the flue-curing conditions. To effectively control the flue-curing and guarantee quality, it is necessary to quantify the chemical composition of tobacco leaves and provide timely feedback during this process. Therefore, the practicability of on-line monitoring of moisture, starch, protein, and soluble sugars for tobacco leaves by near-infrared (NIR) spectroscopy and deep transfer learning was explored. The results showed that the use of an NIR spectrometer equipped with a fiber-optic probe with a deep learning algorithm accurately predicted the content of these components during the curing process. The convolutional neural networks model showed greater potential for on-line monitoring than partial least squares and support vector machines. Furthermore, a network-based deep transfer learning strategy was crafted to include seasonal and temperature variability to accurately predict samples from a new harvest season in a curing barn. The overall studies indicated the efficacy of NIR diffuse reflectance spectroscopy as a rapid and nondestructive method for on-line and simultaneous determination of moisture, starch, protein, and soluble sugars in the flue-curing process to assist in making decisions.

Keywords:

• Convolutional neural networks
• deep transfer learning
• near-infrared spectroscopy
• on-line monitoring
• tobacco leaf flue-curing

Disclosure statement

The authors report there are no conflicts of interest.

Data availability statement

The data presented in this article are available upon request from the corresponding author.

Additional information

Funding

This work was supported by the Science and Technology Project of Guizhou Tobacco Company under grant number 201922, the Science and Technology Fund of Guizhou Province [2019]1070, and the Guizhou Kehe Platform Talents under grant number [2020]4004.