Abstract This research demonstrates a rapid detection method of jointly using electronic nose (E-nose) and computer vision system (CVS) to detect tea aroma and tea appearance for tea quality identification. Feature-level and decision-level fusion strategies were introduced for analyzing the fusion signals of E-nose and CVS. K-nearest neighbors (KNN), support vector machine (SVM) and multinomial logistic regression (MLR) were applied for classification modelling. The results showed that the decision making based on fusion strategies synergistically integrated the advantages of E-nose and CVS and obtained better performance than independent decision in tea quality identification. The decision-level fusion combining the SVM results of both E-nose and CVS was the most effective strategy with the classification accuracy rates of 100% for training and testing sets. This study manifests the simultaneous utilization of E-nose and CVS combined with the decision-level fusion strategy could be worked as a rapid detection method to identify tea quality.

中文翻译：

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电子鼻和计算机视觉结合协同数据融合策略快速识别茶叶质量

摘要 本研究展示了一种联合使用电子鼻（E-nose）和计算机视觉系统（CVS）来检测茶香和茶外观以进行茶质量鉴定的快速检测方法。引入特征级和决策级融合策略来分析E-nose和CVS的融合信号。K-最近邻 (KNN)、支持向量机 (SVM) 和多项逻辑回归 (MLR) 被应用于分类建模。结果表明，基于融合策略的决策协同整合了E-nose和CVS的优势，在茶叶品质识别中获得了优于独立决策的性能。结合 E-nose 和 CVS 的 SVM 结果的决策级融合是最有效的策略，训练和测试集的分类准确率为 100%。本研究表明，同时利用电子鼻和 CVS 结合决策级融合策略可以作为一种快速检测方法来识别茶叶质量。