Abstract
This work proposes a tea-category identification (TCI) system, which can automatically determine tea category from images captured by a 3 charge-coupled device (CCD) digital camera. Three-hundred tea images were acquired as the dataset. Apart from the 64 traditional color histogram features that were extracted, we also introduced a relatively new feature as fractional Fourier entropy (FRFE) and extracted 25 FRFE features from each tea image. Furthermore, the kernel principal component analysis (KPCA) was harnessed to reduce 64 + 25 = 89 features. The four reduced features were fed into a feedforward neural network (FNN). Its optimal weights were obtained by Jaya algorithm. The 10 × 10-fold stratified cross-validation (SCV) showed that our TCI system obtains an overall average sensitivity rate of 97.9%, which was higher than seven existing approaches. In addition, we used only four features less than or equal to state-of-the-art approaches. Our proposed system is efficient in terms of tea-category identification.
Highlights
Tea is a beverage prepared by pouring boiled water over its leaves [1]
We introduced a relatively new image feature of Fractional Fourier Entropy (FRFE), denoted by symbol E
To build the forward neural network (FNN) to be equal to train the weights/biases of all neurons in the FNN, which is treated as an optimization problem, i.e., we need to obtain the optimal weights/biases in order to treated as an optimization problem, i.e., we need to obtain the optimal weights/biases in order to make make minimal the mean-squared error (MSE) between real outputs and target outputs
Summary
Tea is a beverage prepared by pouring boiled water over its leaves [1]. As a medicinal drink, there is increasing evidence that antioxidants contained in tea could help resist diseases, such as breast cancer [2], neurodegenerative disease [3], skin cancer [4], Parkinson’s disease [5], prostate tumor [6], Alzheimer’s disease [7], cardiovascular disease [8], colon cancer [9], etc. There are two novel types of methods, viz., hardware and software, for tea classification. The former is aimed at devising new devices while the latter is aimed at designing new algorithms based on computer vision, which has proven to be better than human vision in many fields [11,12,13]. Zhang et al [25] combined three different types of features (shape, color, and texture) to identify fruit images. They combined fuzzy technique with support vector machine (SVM), and named it fuzzy SVM (FSVM) Their method yielded a promising result of 97.77% recall rate.
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