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Rapid image detection and recognition of rice false smut based on mobile smart devices with anti-light features from cloud database
Ning Yang1,2; Kangpeng Chang1; Sizhe Dong2; Jian Tang3; Aiying Wang3; Rubing Huang4; Yanwei Jia2
2022-06
Source PublicationBiosystems Engineering
ISSN1537-5110
Volume218Pages:229-244
Abstract

Using deep learning-based static image processing technology to identify crop diseases has become an important topic in recent years. However, this technology usually requires a good network environment and powerful computing equipment. It is not conducive for farmers to identify rice diseases on-site in under-developed areas with poor network signals. To address this problem, a crop disease mobile identification system that can adapt to a poor network environment is proposed. Rice morphological characteristics are used for support vector machine (SVM) model to realise offline recognition of rice false smut (RFS) analyzed by histogram of oriented gradient (HOG), circumscribed rectangle aspect ratio (CRAR) features and tilt correction algorithms. Images of rice lesions were obtained through field shooting. These images were used to build a database to train a cloud convolutional neural networks (CNN) recognition model to correct the offline recognition results when the device is in a poor network environment. This database can improve the lack of adaptability of ordinary public databases in the field environment. Moreover, this system is compressed into smart phones to facilitate on-site identification by farmers. This system has a 98% recognition rate of RFS and a recognition speed of 4s. In the case of low specification equipment configuration and a poor field network environment, this system is superior to other recent feature extraction methods.

KeywordCnn Model Mobile Rfs Disease Identification System Offline Image Recognition On-line Co-ordinated Correction
DOI10.1016/j.biosystemseng.2022.04.005
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaAgriculture
WOS SubjectAgricultural Engineering ; Agriculture, Multidisciplinary
WOS IDWOS:000800375700010
PublisherACADEMIC PRESS INC ELSEVIER SCIENCE, 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495
Scopus ID2-s2.0-85129575253
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF ANALOG AND MIXED-SIGNAL VLSI (UNIVERSITY OF MACAU)
INSTITUTE OF MICROELECTRONICS
Corresponding AuthorJian Tang; Yanwei Jia
Affiliation1.School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
2.State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau, China
3.State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China
4.Faculty of Information Technology, Macau University of Science and Technology, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Ning Yang,Kangpeng Chang,Sizhe Dong,et al. Rapid image detection and recognition of rice false smut based on mobile smart devices with anti-light features from cloud database[J]. Biosystems Engineering, 2022, 218, 229-244.
APA Ning Yang., Kangpeng Chang., Sizhe Dong., Jian Tang., Aiying Wang., Rubing Huang., & Yanwei Jia (2022). Rapid image detection and recognition of rice false smut based on mobile smart devices with anti-light features from cloud database. Biosystems Engineering, 218, 229-244.
MLA Ning Yang,et al."Rapid image detection and recognition of rice false smut based on mobile smart devices with anti-light features from cloud database".Biosystems Engineering 218(2022):229-244.
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