Liu Chunlin, Lu Weiying, Gao Boyan, Kimura Hanae, Li Yanfang, Wang Jing
Beijing Advanced Innovation Center for Food Nutrition and Human Health Beijing Technology & Business University (BTBU) Beijing China.
Institute of Food and Nutraceutical Science School of Agriculture and Biology Shanghai Jiao Tong University Shanghai China.
Food Sci Nutr. 2020 Mar 3;8(4):1968-1977. doi: 10.1002/fsn3.1484. eCollection 2020 Apr.
Seven commercial Chinese chrysanthemum tea products were classified by computer vision combined with machine learning algorithms. Without the need of building any specific hardware, the image acquisition was achieved in two computer vision approaches. In the first approach, a series of multivariate classification models were built after morphological feature extraction of the image. The best prediction accuracies when classifying flowering stages and tea types were respectively 90% and 63%. In comparison, the deep neural network was applied directly on the raw image, yielded 96% and 89% correct identifications when classifying flowering stage and tea type, respectively. The model can be applied for rapid and automatic quality determination of teas and other related foods. The result indicated that computer vision, especially when combined with deep learning or other machine learning techniques can be a convenient and versatile method in the evaluation of food quality.
通过计算机视觉结合机器学习算法对七种市售菊花茶产品进行了分类。无需构建任何特定硬件，采用两种计算机视觉方法实现了图像采集。在第一种方法中，对图像进行形态特征提取后构建了一系列多变量分类模型。对开花阶段和茶类进行分类时，最佳预测准确率分别为90%和63%。相比之下，将深度神经网络直接应用于原始图像，对开花阶段和茶类进行分类时，正确识别率分别为96%和89%。该模型可用于茶叶及其他相关食品的快速自动质量判定。结果表明，计算机视觉，尤其是与深度学习或其他机器学习技术相结合时，可成为评估食品质量的一种便捷且通用的方法。
