| Autor: |
DU Haina, MENG Lingfeng, WANG Songfeng, ZHANG Binghui, WANG Aihua, LIU Hao, LI Zengsheng, SUN Fushan |
| Zdroj: |
Tobacco Science & Technology; 2022, Vol. 55 Issue 9, p81-88, 8p |
| Abstrakt: |
To accurately predict the dehydration rate of tobacco leaf during bulk curing and precisely control curing process parameters, three models for predicting the dehydration rate of tobacco leaf were established based on machine learning. Taking the middle leaves of cv. CB-1 as the material, the images and dehydration rates of tobacco leaves were realtimely collected during curing process. Image processing technology was used to extract 10 color features and 10 textural features of the leaves, and 2 color features (a*/b*, R) and 2 textural features (gradient entropy, nonuniformity of gradient distribution) were selected through variable clustering and Pearson correlation analysis. The three established prediction models, the grid search support vector machine (GS-SVM), genetic algorithm optimized BP neural network (GA-BP), extreme learning machine (ELM) models, were subject to training with the four selected features of the images in the training set and the dehydration rates of tobacco leaves. The dehydration rates of tobacco leaves predicted by the three models were compared with the actual dehydration rates. The results showed that all the three prediction models could accurately predict the dehydration rates of tobacco leaves during bulk curing with the root mean square error (RMSE) ≤0.014 0 and coefficient of determination (R²) ≥0.996 1. The prediction errors for the leaves at yellowing stage (0-40 h) and color-fixing stage (40-100 h) were lower than those at stem-drying stage (100-140 h). This technology provides a support for the development of intelligent control system for tobacco leaf curing. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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