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.

中文翻译：

---

基于视觉特征的决策树和模糊推理系统在红绿茶质量分类建模中的应用

在这项研究中，开发了基于模糊决策树（DT）的计算机视觉系统，用于对伊朗常见茶进行分类。使用CCD相机捕获了不同茶类的图像，包括五种绿茶和五种红茶。从图像中总共提取了83种基于图像的信息（包括18种颜色，13种纹理和52种基于小波的特征），并将其引入DT分类器中以区分不同的茶类别。减少错误修剪（REP）和J48 DT用作分类器，并且两种茶的DT的总体准确度等于94.79％，用于区分所有茶类。但是，这些DT具有几乎复杂的结构，因此不适合根据其结构开发Mamdani模糊系统。所以，为了简化DT的结构，使其更适合于开发模糊集，我们对DT进行了分类，将其分为红茶和绿茶。对于红茶和绿茶的分类，J48 DT的总体准确度分别为92.917％和95.000％，对于REP树，其准确度分别等于90.83％和97.08％。由于REP树的结构更简单，即使J48和REP树的性能几乎相同，也使用REP树结构来设置模糊系统的隶属函数和规则。得出的结论是，基于DT的模糊逻​​辑系统可以有效地基于图像提取特征对茶的不同质量类别进行分类。红茶和绿茶的分类分别为917％和95.000％，REP树的准确度分别为90.83％和97.08％。由于REP树的结构更简单，即使J48和REP树的性能几乎相同，也使用REP树结构来设置模糊系统的隶属函数和规则。得出的结论是，基于DT的模糊逻​​辑系统可以有效地基于图像提取特征对茶的不同质量类别进行分类。红茶和绿茶的分类分别为917％和95.000％，REP树的准确度分别为90.83％和97.08％。由于REP树的结构更简单，即使J48和REP树的性能几乎相同，也使用REP树结构来设置模糊系统的隶属函数和规则。得出的结论是，基于DT的模糊逻​​辑系统可以有效地基于图像提取特征对茶的不同质量类别进行分类。