# Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm
## 7 Conclusions  
Our contributions cover the following points: (i) We introduced a novel feature--fractional Fourier entropy (FRFE)--and showed its effectiveness in extracting features for tea images; (ii) We proposed a novel classifier--Jaya-FNN--by combining feedforward neural network with Jaya algorithm; (iii) We demonstrated that our TCI system was better than seven state-of-the-art approaches in terms of overall ASR and, meanwhile, our system used the least features. In the future, we will test other newly proposed feature-extraction methods, such as displacement field [58; 59] and image moments [60]. We will try to improve the classification performance, by introducing new swarm intelligence methods to train FNN classifier, or by introducing newly proposed classifiers, such as variants of SVMs. Besides, our method may be applied to X-ray image, magnetic resonance image, Alzheimer's disease images, and microglia image. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & \\ \hline \hline
**Original** & **Reduced** & **Classification** & **Green** & **Oolong** & **Black** & **Overall** & **Rank** \\
**Features** & **Feature \#** & **Method** & & & & & \\ \hline
3735 & \multirow{2}{*}{5} & \multirow{2}{*}{SVM [15]} & \multirow{2}{*}{90\%} & \multirow{2}{*}{100\%} & \multirow{2}{*}{95\%} & \multirow{2}{*}{95\%} & \multirow{2}{*}{7} \\ spectrum & & & & & & & \\ \hline
8 metal & 8 & BPNN [16] & – & – & – & 95\% & 7 \\ \hline
12 color, & \multirow{2}{*}{11} & \multirow{2}{*}{LDA [21]} & \multirow{2}{*}{96.7\%} & \multirow{2}{*}{92.3\%} & \multirow{2}{*}{98.5\%} & \multirow{2}{*}{95.8\%} & \multirow{2}{*}{6} \\
12 texture & & & & & & & \\ \hline
2 color, & \multirow{2}{*}{8} & \multirow{2}{*}{GNN [22]} & \multirow{2}{*}{95.8\%} & \multirow{2}{*}{94.4\%} & \multirow{2}{*}{97.9\%} & \multirow{2}{*}{96.0\%} & \multirow{2}{*}{5} \\
6 shape & & & & & & & \\ \hline
64 CH, & \multirow{2}{*}{14} & \multirow{2}{*}{FSCABC-FNN [25]} & \multirow{2}{*}{98.1\%} & \multirow{2}{*}{97.7\%} & \multirow{2}{*}{96.4\%} & \multirow{2}{*}{97.4\%} & \multirow{2}{*}{3} \\
8 shape & & & & & & & \\ \hline
64 CH, & \multirow{2}{*}{5} & \multirow{2}{*}{SVM + WTA [28]} & \multirow{2}{*}{95.7\%} & \multirow{2}{*}{98.1\%} & \multirow{2}{*}{97.9\%} & \multirow{2}{*}{97.23\%} & \multirow{2}{*}{4} \\
16 WPE & & & & & & & \\ \hline
64 CH, & \multirow{2}{*}{5} & \multirow{2}{*}{SVM + WTA [28]} & \multirow{2}{*}{96.2\%} & \multirow{2}{*}{98.8\%} & \multirow{2}{*}{98.3\%} & \multirow{2}{*}{97.77\%} & \multirow{2}{*}{2} \\
16 WPE & & & & & & & \\ \hline \hline \multicolumn{8}{c}{**Proposed Approaches**} \\ \hline
64 CH, & \multirow{2}{*}{4} & \multirow{2}{*}{Jaya-FNN [25]} & \multirow{2}{*}{97.3\% \(\pm\) 0.4\%} & \multirow{2}{*}{98.9\% \(\pm\) 0.3\%} & \multirow{2}{*}{97.6\% \(\pm\) 0.3\%} & \multirow{2}{*}{97.9\% \(\pm\) 0.3\%} & \multirow{2}{*}{1} \\
25 FRFE & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average sensitivity rate (ASR) over a 10 \(\times\) 10-fold stratified cross validation (SCV). **Acknowledgments:** This paper was supported by the National Natural Science Foundation of China (NSFC) (51407095, 61503188), the Natural Science Foundation of Jiangsu Province (BK20150983, BK20150982, BK20150973, BK20151548), the Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (BM2013006), the Key Supporting Science and Technology Program (Industry) of Jiangsu Province (BE2012201, BE2013012-2, BE2014009-3), the Program of Natural Science Research of Jiangsu Higher Education Institutions (15 KJB470010, 15 KJB510018, 15 KJB510016, 13 KJB460011, 14 KJB480004, 14 KJB520011), the Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province (BA2013058), the Nanjing Normal University Research Foundation for Talented Scholars (2013191 XGQ0061, 2014119 XGQ0080), and the Open Fund of Guangxi Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology (15-140-30-008K).