Collecting external data

The training of the predictive model requires the acquisition of a large amount of data, so the speed of data collection needs to be guaranteed. If the external quality characteristics of food are extracted in an artificial way, it will undoubtedly consume a lot of time. As a branch of deep learning, computer vision [13, 14, 15, 16, 17, 18, 19] has been widely used in food classification and feature extraction. For every food features are figured out for which the quality is either directly or inversely varied.
Computer vision technology is a branch of artificial intelligence that aims to eventually replace the human visual decision-making process with automated programs. Computer vision technology is a mechanism that artificially simulates the human thinking process. Through continuous learning, it can make accurate, fast and complex judgments. At present, the more mature solution in computer vision technology is CNN (convolutional neural network). The difference from ANN is that CNN introduces the operation of convolution. CNN are mainly composed of these types of layers: input layer, convolutional layer, pooling layer and fully connected layer. The core part is the convolution layer and the pooling layer. The function of the convolutional layer is to extract the information in the input image, which is called image features. These features are reflected by each pixel in the image in a combined or independent way, such as texture features and color features of the image. The function of the convolution layer is to perform convolution operations. The convolution operation is to perform the cross-correlation operation from left to right and from top to bottom through the convolution check of the matrix of each channel, and slide from the upper left corner to the lower right corner step by step. The sliding step size is a hyper parameter. It means that the corresponding positions are multiplied and then added, and finally the values of the three channels are also added together to obtain a value. The role of the pooling layer is to select the features extracted from the convolutional layer. The basic structure of the convolutional layer and the pooling layer is shown in Figure 1(B)
The convolutional neural network can not only improve the detection speed of acquiring the external features of food, but also realize automatic detection based on this technology. Therefore, convolutional neural network becomes a new solution for food external feature extraction