Application of Image Wavelet Transform for Qualitative Classification of Green Tea Using Metaheuristic Algorithms

This study was aimed to investigate the best features extracted from images to determine the best technique for qualitative classification of green tea by using meta-heuristic algorithms. Five different classes of green tea were evaluated according to the standards of the Institute of Standards and Industrial Research of Iran. After receiving the images of different green tea classes in the computer, 6 square blocks were extracted from each of the original color images. These image blocks were transformed from RGB to gray scale images. One-level discrete Haar wavelet filter was applied to the gray images and 4 wavelet subimages were obtained. Co-occurrence matrices were calculated for each wavelet subimages and 17 common texture features in the image textural studies, were extracted from subimages (totally 68 texture features for each block image). By using principal component analysis, 8 feature components were produced from the original features and used for the separation of 5 groups of green tea. The results showed that algorithms of artificial neural networks, support vector machine and decision tree were capable of qualitative classification of green tea with high accuracy. However, Bayesian network did not have acceptable performance. According to the evaluation statistics, the multilayer perceptron artificial neural networks (with Kappa statistic, root mean square error and classification accuracy of 0.90, 0.42, and 99.17%, respectively) was the best classifier. Based on the results of this study, the use of machine vision and texture features extracted from image wavelet subimages is a suitable technique for the qualitative classification of green tea.