ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1604382
This article is part of the Research TopicMachine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture, Volume IIView all 32 articles
Intelligent Recognition of Tobacco Leaves States during curing with Deep Neural Network
Provisionally accepted
Qiang Xu1
Yanling Zhang1Aiguo Wang1Guangqing Chen2Xianjie Cai3Shuoye Zhou2Junying Li4Baofeng Jin5Ding Yan3Jiajie Huang5Zuxiao Chen6Heng Zhang4Jianwei Wang1Weimin Guo1*Jianjun Liu2*- 1Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, China
- 2Henan Provincial Tobacco Company of CNTC, Zhengzhou, China
- 3Shanghai Tobacco Company, Shanghai, China
- 4Pingdingshan Tobacco Company, Henan Provincial Tobacco Company, Pingdingshan, Henan Province, China
- 5China Tobacco Guangdong Idustrial Co., Ltd, Guangzhou, China
- 6Jilin Tobacco Idustrial Co., Ltd, Changchun, China
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The state monitoring of tobacco leaves during the curing process is crucial for process control and automation of tobacco agricultural production. While most of the existing research on tobacco leaves state recognition focused on the temporal state of the leaves, the morphological state was often neglected. Moreover, the previous research typically used a limited number of non-industrial images for training, creating a significant disparity with the images encountered in actual applications. To investigate the potential of deep learning algorithms in identifying the morphological states of tobacco leaves in real industrial scenarios, a comprehensive and large-scale dataset was developed in this study. This dataset focused on the states of tobacco leaves in actual bulk curing barn in multiple production areas in China, specifically recognizing the degrees of yellowing, browning, and drying. Then, an efficient deep learning method was proposed based on this dataset to enhance the predictive performance. The prediction accuracy achieved for the yellowing degree, browning degree, and drying degree were 83.0%, 90.5%, and 75.6% respectively. The overall average accuracy, satisfied the requirements of practical application scenarios with a value of 83%. It can be leveraged for service curing parameters optimization and tobacco quality improvements in curing processes.
Keywords: Tobacco leaves, Large-scale dataset, Bulk curing barn, Image Recognition, deep learning
Received: 07 Apr 2025; Accepted: 06 Jun 2025.
Copyright: © 2025 Xu, Zhang, Wang, Chen, Cai, Zhou, Li, Jin, Yan, Huang, Chen, Zhang, Wang, Guo and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Weimin Guo, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, China
Jianjun Liu, Henan Provincial Tobacco Company of CNTC, Zhengzhou, China
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