Residential College | false |
Status | 已發表Published |
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 Publication | Biosystems Engineering |
ISSN | 1537-5110 |
Volume | 218Pages: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. |
Keyword | Cnn Model Mobile Rfs Disease Identification System Offline Image Recognition On-line Co-ordinated Correction |
DOI | 10.1016/j.biosystemseng.2022.04.005 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Agriculture |
WOS Subject | Agricultural Engineering ; Agriculture, Multidisciplinary |
WOS ID | WOS:000800375700010 |
Publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE, 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 |
Scopus ID | 2-s2.0-85129575253 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | THE STATE KEY LABORATORY OF ANALOG AND MIXED-SIGNAL VLSI (UNIVERSITY OF MACAU) INSTITUTE OF MICROELECTRONICS |
Corresponding Author | Jian Tang; Yanwei Jia |
Affiliation | 1.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 Affilication | University of Macau |
Corresponding Author Affilication | University 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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