Abstract
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.
Highlights
The chrysanthemums are the flowering perennial plants with enormous horticultural varieties and cultivars
A convolution layer which performs convolution operation over an image, with the number of parameters to be learned in these filters is equal to the number of elements of these filters; a nonlinear rectified linear units (ReLU) layer that perform a transformation similar to the sigmoid function are placed right under each of the convolution layers; a batch normalization layer which normalize outputs from the previous layer to speed up training of convolutional neural networks; a pooling layer reduces the size of the image by performing a sliding-window averaging function to subsample the original image
The computer vision system (CVS) combined with multivariate classification and deep neural network (DNN) was examined in the quality detection of chrysanthemum teas without requiring any specific hardware
Summary
The chrysanthemums are the flowering perennial plants with enormous horticultural varieties and cultivars. With the help of artificial intelligence, automated decisions can be made with complex but mathematically assured models, such that objective, accurate, convenient, and rapid quality detections and identifications can be achieved. Such approach is suitable for large-scale, on-line, or atline manufacturing of food products. The resulted model was aimed to achieve the automatic discrimination of different types and characteristics of chrysanthemum teas This approach may help to understand the relationships between appearance and functional components of chrysanthemum teas in the future
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