Research On The Grading Method Of Jujube Appearance Quality Based On Convolutional Neural Network

The jujube(Zizyphus jujuba Mill.),which is one of the most important and representative fruits in China,is admired for its high nutritional value as a type of important traditional Chinese medicinal and tonic food.This fruit plays an important role as a food industrial raw material.The overall quality of jujube is impacted more severely in the course of jujube's picking and transportation,due to the mixture of different dried jujube qualities,such as starchy head,cracked skin,mildewed and wormy fruit.The economic benefits of jujube growers and jujube industry will be strongly affected by the jujube's quality.The market prices of jujube also vary with the quality.Therefore,it is a crucial link to identify the defective jujubes and sort jujubes into different qualities for the storage,transportation and further processing of jujubes.However,the quality of jujube classification is primarily manual at present,and it has a number of shortcomings,including high labour intensiveness,high cost,and low efficiency.To meet the demands of the market and the jujube processing industry,it is necessary to identify an automatic,efficient and nondestructive method for the multi-classification of dried jujube.A novel method based on a double branch deep Fusion convolution neural network(DDFnet)is developed to classify dried jujubes.First,the structure of this network is designed as double branches.In one branch,the dataset of dried jujubes is pre-trained with a model trained by SqueezeNet on the 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.It introduces the BN layer(Batch Normalization)and PReLU activation layer in this branch to accelerate the convergence rate of network.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.