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Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features

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Abstract

A fuzzified decision tree (DT)-based computer vision system was developed in this study for classification of common Iranian teas. Images of different tea categories, including five green tea and five black tea classes were captured using a CCD camera. In total, 83 image-based information (including 18 colour, 13 texture, and 52 wavelet-based features) were extracted from the images and introduced into DT classifier to distinguish between different tea categories. Reduced Error Pruning (REP) and J48 DTs were applied as classifier and the overall accuracy of both evaluated DTs was equal to 94.79% for differentiating all tea categories. However, these DTs had almost complex structures which made them unsuitable for developing a Mamdani fuzzy system, based on their structure. So, in order to simplify the structures of the DTs to be more suitable for developing fuzzy sets, the DTs were examined for categorizing black and green teas separately. The overall accuracy of J48 DT was 92.917% and 95.000% for classification of black and green teas, respectively and for REP tree the accuracy was equal to 90.83% and 97.08%, respectively. The REP tree structure was used to set the membership functions and rules of the fuzzy system because of the simpler structure of REP tree, even though J48 and REP trees had almost equal performances. It was concluded that the DT-based fuzzy logic system can be used effectively to classify different quality categories of tea, based on image extracted features.

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Acknowledgements

The authors would like to thank the University of Guilan for providing the facilities and financial support for this research.

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Correspondence to Hemad Zareiforoush.

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Bakhshipour, A., Zareiforoush, H. & Bagheri, I. Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features. Food Measure (2020). https://doi.org/10.1007/s11694-020-00390-8

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Keywords

  • Classification
  • Computer vision
  • Decision tree
  • Fuzzy logic
  • Tea