Modeling of the Bulk Tobacco Flue-Curing Process Using a Deep Learning-Based Method

doi:10.1109/ACCESS.2021.3119544

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	Modeling of the Bulk Tobacco Flue-Curing Process Using a Deep Learning-Based Method
	Wu, Juan 1,2; Yang, Simon X.3
	2021
发表期刊	IEEE Access
ISSN	2169-3536
EISSN	2169-3536
卷号	9 页码:140424-140436
摘要	The research of the bulk tobacco flue-curing process is helpful to model the intelligent bulk curing system, which is designed to implement the curing procedure without manual operation. An intelligent bulk curing method based on the convolutional neural network (CNN) named TobaccoNet was proposed, which could set the target dry-bulb temperature (T-D) and the target wet-bulb temperature ( T-S) of the bulk curing barn according to the tobacco leaves image. The performance of the TobaccoNet is compared with the traditional manual image feature extraction method, the stacked-sparse-autoencoder (SSAE)-based deep learning method, and the other two methods applied in related references. The test results show that TobaccoNet outperforms the comparison methods in predicting T-D and T-S. Specifically, the correlation coefficient reaches 0.9965 and 0.9683, the mean relative error is 1.62% and 1.77%, and the root mean squared error achieves 1.061 degrees C and 0.8581 degrees C respectively. The promising results demonstrate that TobaccoNet is effective and reliable for modeling the intelligent bulk tobacco flue-curing process. The influence of different CNN structures on the prediction accuracy ofT(D)and T(S)was analyzed. From the perspective of the computational complexity and the prediction performance, the proposed sequential CNN structure is more suitable for analyzing bulk tobacco curing in this study.
关键词	Curing Convolutional neural networks Feature extraction Deep learning Temperature measurement Schedules Process control Convolutional neural network bulk tobacco flue-curing tobacco leaves deep learning Convolution Extraction Flues Forecasting Mean square error Neural networks Tobacco Bulk tobacco flue curing Curing process Dry bulb temperatures Features extraction Schedule Tobacco leaf
DOI	10.1109/ACCESS.2021.3119544
收录类别	SCIE ; EI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000709060400001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI入藏号	20214311048050
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 716.1 Information Theory and Signal Processing ; 802.2 Chemical Reactions ; 802.3 Chemical Operations ; 821.4 Agricultural Products ; 922.2 Mathematical Statistics ; 944.6 Temperature Measurements
原始文献类型	Article ; Journal article (JA)
出版地	PISCATAWAY
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/3743
专题	重庆电子科技职业大学
作者单位	1.Chongqing Univ, Sch Microelect & Commun Engn, Chongqing 400044, Peoples R China; 2.Chongqing Coll Elect Engn, Chongqing 401331, Peoples R China; 3.Univ Guelph, Sch Engn, Adv Robot & Intelligent Syst ARIS Lab, Guelph, ON N1G 2W1, Canada
推荐引用方式 GB/T 7714	Wu, Juan,Yang, Simon X.. Modeling of the Bulk Tobacco Flue-Curing Process Using a Deep Learning-Based Method[J]. IEEE Access,2021,9:140424-140436.
APA	Wu, Juan,&Yang, Simon X..(2021).Modeling of the Bulk Tobacco Flue-Curing Process Using a Deep Learning-Based Method.IEEE Access,9,140424-140436.
MLA	Wu, Juan,et al."Modeling of the Bulk Tobacco Flue-Curing Process Using a Deep Learning-Based Method".IEEE Access 9(2021):140424-140436.

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