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Sensors 2019, 19(8), 1778; https://doi.org/10.3390/s19081778

Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches

1,2 and 3,*
1
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
2
Chongqing College of Electronic Engineering, Chongqing 401331, China
3
School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
*
Author to whom correspondence should be addressed.
Received: 7 March 2019 / Revised: 5 April 2019 / Accepted: 8 April 2019 / Published: 13 April 2019
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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Abstract

The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer’s control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results’ robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule. View Full-Text
Keywords: multi-sensor data fusion; electronic nose; bulk tobacco curing schedule; least squares support vector machines; adaptive neuro-fuzzy inference system multi-sensor data fusion; electronic nose; bulk tobacco curing schedule; least squares support vector machines; adaptive neuro-fuzzy inference system
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wu, J.; Yang, S.X. Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches. Sensors 2019, 19, 1778.

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