Offline mobile diagnosis system for citrus pests and diseases using deep compression neural network

Abstract

This study presents an offline mobile diagnosis system for citrus pests and diseases by compression convolutional neural network. Recently, with the growth of labelled data, the deep neural network incites the revolutionary change with a quantum leap in various fields. Benefiting from the backpropagation method, the proper network structure can automatically extract high‐level representations and find corresponding labels. The authors made use of the advantages of the deep neural network to design an android application, which can be installed in any stand‐alone devices to instantaneously identify the citrus pests and diseases. The proposed diagnosis system has three characteristics: low cost, low latency and high accuracy. These characteristics contribute to make the professional offline prediction for avoiding further economic loss caused by disease spreading. In order to validate the proposed system, the authors conducted thorough evaluations on two data sets, ‘citrus pests and diseases’, CIFAR, which show the superiority of the proposed approach in terms of the accuracy and the number of model parameters.

Keywords

• disease spreading
• citrus pests
• diseases
• offline mobile diagnosis system
• deep compression neural network
• compression convolutional neural network