Dried Jujube Classification Based on a Double Branch Deep Fusion Convolution Neural Network

Abstract

A novel method based on a double branch deep fusion convolution neural network (DDFnet) is developed to classify dried jujubes. First, the structure of the network is designed as double branches. In one branch, the dataset of the jujubes is pre-trained with a model trained by a Squeezenet network on a large-scale ImageNet dataset. The other branch is founded on the structure of Squeezenet, which is composed of fire modules. The feature maps that are output by squeeze and expand convolution layers are fused into fusion modules. Next, a model trained on the dataset with DDFnet is used to achieve the multi-classification of jujubes. Finally, the dataset is classified by the model; it shows good performance with high accuracy rates of 99.6%, 99.8%, 98.5%, and 99.2% for the classification of plump, wizened, cracked, and defective jujubes, respectively. This research demonstrates the feasibility of DDFnet for sorting dried jujubes and enhancing product quality.