利用计算机视觉识别茶叶的色泽类型

Identification of tea color by using computer vision

针对茶叶色泽的感官评定存在识别结果的主观性强和一致性差等缺点,提出了一种新的识别方法,在计算机视觉技术定量描述茶叶的颜色特征的基础上,根据相似分类法(SIMCA)模式识别原理分别为碧螺春、龙井和祁红等三种茶叶建立了各自的分类识别模型并进行识别.结果显示在显著性水平α=5%的条件下,所建立的模型最佳;训练时,各自模型对己类样本的回判率和对非己类样本的拒绝率都达到100%;预测时,各自模型对己类样本的识别率分别为90%、90%和100%,对非己类样本的拒绝率都是100%.试验结果表明,利用计算机视觉技术识别茶叶的色泽类型是可行的.

To overcome the deficiency in tea sensory evaluation such as the result subjectivity and poor coherence, a new method of tea identification was proposed based on soft independent modeling of class analogy pattern recognition theory. By using computer vision to quantitatively depict tea color characters, three predictive models for Biluochun, Longjing, and Qihong teas were built. Under the α = 5% significance level, the three predictive models were the best. The results showed that, the back estimation rates for own class samples and rejection rates for other class samples of three models are all 100% in training; the identification rates of three models for own class samples are 90%, 90%, and 100% respectively in prediction, while the rejection rates for other class samples of three models are all 100%. The experimental results showed that the method is feasible with computer vision identifying tea categories based on the tea color characteristics.