Research on image recognition of three fritillaria cirrhosa species based on deep learning

Abstract

Abstract Based on the deep learning method, a network model that can quickly and accurately identify the species of fritillaria cirrhosa species was constructed. Taking three kinds of fritillaria cirrhosa images, the learning method based on deep residual convolutional neural network was used to input the unprocessed original image directly as input, and the features of the image were extracted through convolution and pooling operations. On this basis, the ResNet34 model was improved, and the additional fully connected layer was added in front of the Softmax classifier to improve the learning ability of the network model. Visual analysis of the training process was carried out to determine the optimal number of iterations for model training and ensure the recognition accuracy. Total of 3915 images of three kinds of fritillaria cirrhosae were used as data sources for the experiments, among which 160 images of each type were randomly selected to form the validation set, and 60 Songbei, 54 Qingbei, and 58 Lubei images were selected to form the test set. The final training set recognition accuracy rate was 95.8%, the validation set accuracy rate reached 92.3%, and the test set accuracy rate was 88.7%. The image recognition method of fritillaria cirrhosa based on deep learning proposed in this paper is effective and feasible, which can quickly and accurately identify the species of fritillaria cirrhosa species, and provides a new idea for the intelligent recognition of Chinese medicinal materials.