{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T21:40:25Z","timestamp":1779918025121,"version":"3.53.1"},"reference-count":43,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,4,13]],"date-time":"2019-04-13T00:00:00Z","timestamp":1555113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chongqing Municipal Education Commission of China","award":["KJ1503002"],"award-info":[{"award-number":["KJ1503002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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\u2019 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.<\/jats:p>","DOI":"10.3390\/s19081778","type":"journal-article","created":{"date-parts":[[2019,4,15]],"date-time":"2019-04-15T11:15:58Z","timestamp":1555326958000},"page":"1778","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7241-2771","authenticated-orcid":false,"given":"Juan","family":"Wu","sequence":"first","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"},{"name":"Chongqing College of Electronic Engineering, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6888-7993","authenticated-orcid":false,"given":"Simon X.","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14004","DOI":"10.3390\/s121014004","article-title":"Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks","volume":"12","author":"Aguiar","year":"2012","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.fct.2006.08.024","article-title":"Effect of A Flue-curing Process That Reduces Tobacco Specific Nitrosamines on the Tumor Promotion in SENCAR Mice by Cigarette Smoke Condensate","volume":"45","author":"Hayes","year":"2007","journal-title":"Food Chem. 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