Deep Recognition of Chinese Herbal Medicines Based on a Caputo Fractional Order Convolutional Neural Network

Chinese herbal medicines are the treasure of Chinese traditional medicine, which contains active ingredients that have significant therapeutic effects for many diseases. To solve the fluctuation and inefficiency problems faced by the existing image recognition of Chinese herbal medicines relying on manual subjectivity, a deep classification and recognition method based on a fractional order convolutional neural network is proposed for the images of Chinese herbal medicines. This method introduces Caputo fractional order gradient descent to update the model parameters, which improves the accuracy of gradient descent in the process of training the model, and solves the problem that integer order gradient descent is prone to fall into the local optimal solution and thus leads to low accuracy. By establishing a multi-scale standard Chinese herbal medicines dataset, the feature recognition ability based on Caputo Fractional Order Convolutional Neural Network (CFO-CNN) is trained and tested. And at the same time, the fractional order backpropagation function is used, so that the model can effectively find the global optimal solution, and achieve the output of the feature deep recognition. Experiments show that the method can effectively achieve the deep classification recognition of Chinese herbal medicines varieties, species and grades.