A hyperspectral imaging technology based method for identifying the variety of mengding mountain tea

Aiming at disadvantages of labor and time consuming of traditional tea classification method, this paper proposes a tea variety classification algorithm that integrates spectral and image characteristics with Mengding Mountain Huangya tea, Zhuyeqing tea and Ganlu tea of Ya'an City, Sichuan Province as objects. It firstly collected the hyperspectral images of tea samples with “GaiaSorter” hyperspectral sorter. After performing relevant pretreatment, 18 spectral characteristic parameters including red-edge position, absorbing area and absorbing depth were extracted according to the spectral curves and 28 image characteristics including average gray scale, consistency and entropy were extracted according to the images. The confluent characteristics were carried out dimensionality reduction with PCA (principal component analysis) method before they are classified and identified with C-SVM algorithm. Experimental results showed that classification of three varieties of tea can be realized rapidly when the input principal component is selected as 3 and the accuracy rate of identification is up to 100%.