Abstract
Objective:In order to improve the identification efficiency of cut tobacco.Methods:F-score feature selection method and AdaBoost ensemble learning method were used to recognize cut tobacco components. The texture, color and shape features of cut tobacco were extracted as the input of the model. The feature dimension is reduced by F-score feature selection method, and the support vector machine (SVM) was used as the base classifier, then AdaBoost ensemble learning method was used to get the classification model of cut tobacco.Results:This method could effectively distinguish different components of cut tobacco, and the recognition accuracy of each kind of cut tobacco was more than 95%.Conclusion:AdaBoost ensemble learning method is faster and more convenient than traditional methods, and also safer and more effective.
Publication Date
7-7-2022
First Page
205
Last Page
211
DOI
10.13652/j.spjx.1003.5788.2022.90008
Recommended Citation
Xiao-ming, WANG; Jia-xin, WEI; Fei, MA; Yi-bin, WANG; Wen-wu, XU; Ya-wei, JIN; and Qi, LI
(2022)
"Identification of cut tobacco components based on AdaBoost ensemble learning,"
Food and Machinery: Vol. 38:
Iss.
3, Article 33.
DOI: 10.13652/j.spjx.1003.5788.2022.90008
Available at:
https://www.ifoodmm.cn/journal/vol38/iss3/33
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