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

Content Provider	IET Digital Library
Author	You, Jie Lee, Joonwhoan
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.
Starting Page	370
Ending Page	377
Page Count	8
ISSN	17519632
Volume Number	14
e-ISSN	17519640
Issue Number	Issue 6, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/14/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5784
Journal	IET Computer Vision
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-03-13
Access Restriction	Open
Rights License	Creative Commons Attribution-Non Commercial-No Derivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Subject Keyword	Backpropagation Citrus Pests Compression Convolutional Neural Network Computer Vision And Image Processing Technique Corresponding Labels Deep Compression Neural Network Deep Neural Network Disease Spreading Diseases High-level Representations Knowledge Engineering Technique Labelled Data Learning in AI Neural Computing Technique Neural Nets Offline Mobile Diagnosis System Professional Offline Prediction Proper Network Structure
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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