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A Rapidly Diagnosis and Application System of Fusarium Head Blight Based on Smartphone

文献类型： 外文期刊

作者： Zhang, Dongyan 1 ; Wang, Daoyong 1 ; Du, Shizhou 2 ; Huang, Linsheng 1 ; Zhao, Haitao 1 ; Liang, Dong 1 ; Gu, Chunyan 3 ; Yang, Xue 3 ;

作者机构： 1.Anhui Univ, Anhui Engn Lab Agroecol Big Data, Hefei, Peoples R China

2.Anhui Acad Agr Sci, Inst Crops, Hefei, Peoples R China

3.Anhui Acad Agr Sci, Inst Plant Protect & Agroprod Safety, Hefei, Peoples R China

关键词： diagnosis system; feature extraction; CARS-RS-SVM; Fusarium head blight; wheat

期刊名称：2019 8TH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS)

ISSN： 2334-3168

年卷期： 2019 年

页码：

收录情况： SCI

摘要： Wheat (Triticum aestivum L.) is one of the three major cereals worldwide. The FusaHum graminearum Sehw., special fugus always damages the wheat ear, and produces vomitoxin,is difficult to control and prevent, and seriously threatens the health of humans, animals and China's food security. Currently, rapidly, accurately and non-destructively diagnostic devices or systems for this disease have not been disclosed. In this study, the infected ears with different severities were picked up in key growth stages. The diseased area of wheat ear was extracted using hypergreen characteristic, and a total of 30 features of infected ears were chosen including color (Lab, HSI, HSV, YCbCr color space), texture (LBP and LLE dimension reduction), and shape (squareness, shape complexity, and eccentricity). Then using the competitive adaptive re-weighted sampling (CARS) and rough set algorithm (RS) to screen the characteristics of the diseased ear, the four characteristics with the largest contribution were determined to establish the CARS-SVM and CARS-RS-SVM models respectively. The study found that the recognition rate of CARS-SVM model is 85.4%, while CARS-RS-SVM model is 92.7%. Thus the CARS-RS-SVM was thought of as the optimal model by two indicators of identification accuracy. On the basis, a wheat scab diagnosis system based on Android mobile phone was constructed. It consists of three parts - Clients, Service-Terminal and Database. The Client was designed by Android Studio and its functions mainly include image acquisition, image storage, GPS positioning, image uploading and diagnostic results display. The Service-Terminal was completed by the mixed programming of Myeclipse and Matlab software, and Tomcat was used as the Server. It mainly implements the functions of image receiving, image preprocessing, feature extraction and selection, and classifier modeling. The MySQL was used to establish two databases: the Disease Characteristics Database and the Disease Diagnosis Knowledge Base. Finally, through samples testing and validating, the Android-based mobile terminal can real-time collect the image of Fusarium head blight and upload the server. After the target image was processed and compared by the Disease Characteristics Database, the appropriate diagnostic knowledge was selected from the Disease Diagnosis Knowledge Base and feedbacked to the client. In summary, the results of this study showed that it was helpful for the rapid and non-destructive investigation of infected FHB in the field, and it would provide a reference for the study of other crop diseases, facilitate the application and development of new technologies such as artificial intelligence and big data in agriculture.

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