Nondestructive Identification of Longjing Tea Grade by Fusing Spectral and Textural Feature
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Abstract:
The rapid and nondestructive identification of Longjing tea grade was of great significance. In this study, support vector machine (SVM) model was respectively established by using hyperspectral imaging technology with six levels of Longjing tea, based on spectral features, texture features and fusion features. First, standard normal variable (SNV) was used to normalize the spectra, extract the spectral features, and establish the SVM spectral model. Then, the high-dimensional hyperspectral data was mapped to the low-dimensional space through the T-distributed and stochastic neighbor embedding (T-SNE) algorithm, and feature images were selected. Gray-level Co-occurrence matrix (GLCM) was applied to extract texture features and establish a SVM image model. Finally, spectral features and texture features were fused at the data level to establish a SVM mixture model. The results showed that the recognition rate of predictive sets based on spectral model was 91.11%, the recognition rate of predictive sets based on image model was 75.42% and the recognition rate of predictive sets based on mixed model was 95.14%. It illustrated that compared with modeling using only spectral or texture information, combining spectral and texture features can improve the accuracy of identification. In order to further improve the performance of the mixed model, artificial bee colony (ABC) algorithm was introduced to iteratively optimize the penalty factor C and kernel function width g of the SVM model, construct the optimal model, and the accuracy of the prediction sets can be reached 98.61%. The study provides a reliable method to improve the rapid nondestructive assessment technology of Longjing tea.
