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Tea category identification based on optimal wavelet entropy and weighted k-Nearest Neighbors algorithm

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Abstract

Tea category classification is of vital importance to industrial applications. We developed a tea-category identification system based on machine learning and computer vision with the aim of classifying different tea types automatically and accurately. 75 photos of three categories of tea were obtained with 3-CCD digital camera, they are green, black, and oolong. After preprocessing, we obtained 7 coefficient subbands using 2-level wavelet transform, and extracted the entropies from the coefficient subbands as the features. Finally, a weighted k-Nearest Neighbors algorithm was trained for the classification. The experiment results over 5 × 5-fold cross validation showed that the proposed approach achieved sensitivities of 95.2 %, 90.4 %, and 98.4 %, for green, oolong, and black tea, respectively. We obtained an overall accuracy of 94.7 %. The average time to identify a new image was merely 0.0491 s. Our method is accurate and efficient in identifying tea categories.

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Acknowledgments

This paper was supported by NSFC (61602250), Natural Science Foundation of Jiangsu Province (BK20150983), Open Project Program of the State Key Lab of CAD&CG, Zhejiang University (A1616), Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology (2016WLZC013), Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing (BD201607).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, 210042, China

    Xueyan Wu & Jiquan Yang

  2. Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Policy Academy, Changsha, Hunan, 410138, China

    Xueyan Wu & Jiquan Yang

  3. School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, 210023, China

    Shuihua Wang

  4. School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, 210046, China

    Shuihua Wang

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  1. Xueyan Wu
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  2. Jiquan Yang
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Correspondence to Shuihua Wang.

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Xueyan Wu and Jiquan Yang contribute equally to this work

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Wu, X., Yang, J. & Wang, S. Tea category identification based on optimal wavelet entropy and weighted k-Nearest Neighbors algorithm. Multimed Tools Appl 77, 3745–3759 (2018). https://doi.org/10.1007/s11042-016-3931-z

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  • DOI: https://doi.org/10.1007/s11042-016-3931-z

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