Abstract: Focusing on the problem of traditional crop disease recognition methods that the artificially designed features are more susceptible to the crop disease image shapes, illumination and background, a recognition method of jujube disease was proposed based on the internet of things and deep convolutional neural network (DCNN). The network model was composed of input layer, 4 convolutional layers, 3 down-sampling layers, fully-connection layer and output layer. The proposed method can extract effective features of winter jujube disease image and recognize the diseases, avoiding the complicated feature extraction process of the traditional crop disease method. The proposed method was verified on the winter jujube fruit disease database, and the recognition rate was above 92%. The experimental results showed that the proposed method was suitable for winter jujube disease recognition on the large-scale disease database collected by internet of things.

Key words: winter jujube disease recognition, winter jujube disease image, deep convolutional neural network, feature extraction