Offline mobile diagnosis system for citrus pests and diseases using deep compression neural network
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 backpropag...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-09-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2018.5784 |
| _version_ | 1797684724037058560 |
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| author | Jie You Joonwhoan Lee |
| author_facet | Jie You Joonwhoan Lee |
| author_sort | Jie You |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T00:33:53Z |
| format | Article |
| id | doaj.art-1f2d6322bc114c6ca9b1de61b16aa854 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:33:53Z |
| publishDate | 2020-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-1f2d6322bc114c6ca9b1de61b16aa8542023-09-15T10:06:49ZengWileyIET Computer Vision1751-96321751-96402020-09-0114637037710.1049/iet-cvi.2018.5784Offline mobile diagnosis system for citrus pests and diseases using deep compression neural networkJie You0Joonwhoan Lee1Department of Computer EngineeringJeonbuk National University South KoreaJeonjuRepublic of KoreaRCAITDepartment of Computer EngineeringJeonbuk National University South KoreaJeonjuRepublic of KoreaThis 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.https://doi.org/10.1049/iet-cvi.2018.5784disease spreadingcitrus pestsdiseasesoffline mobile diagnosis systemdeep compression neural networkcompression convolutional neural network |
| spellingShingle | Jie You Joonwhoan Lee Offline mobile diagnosis system for citrus pests and diseases using deep compression neural network IET Computer Vision disease spreading citrus pests diseases offline mobile diagnosis system deep compression neural network compression convolutional neural network |
| title | Offline mobile diagnosis system for citrus pests and diseases using deep compression neural network |
| title_full | Offline mobile diagnosis system for citrus pests and diseases using deep compression neural network |
| title_fullStr | Offline mobile diagnosis system for citrus pests and diseases using deep compression neural network |
| title_full_unstemmed | Offline mobile diagnosis system for citrus pests and diseases using deep compression neural network |
| title_short | Offline mobile diagnosis system for citrus pests and diseases using deep compression neural network |
| title_sort | offline mobile diagnosis system for citrus pests and diseases using deep compression neural network |
| topic | disease spreading citrus pests diseases offline mobile diagnosis system deep compression neural network compression convolutional neural network |
| url | https://doi.org/10.1049/iet-cvi.2018.5784 |
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