Application of Support Vector Machine for Detecting Rice Diseases Using Shape and Color Texture Features

Author

Yao, Qing ; Guan, Zexin ; Zhou, Yingfeng ; Tang, Jian ; Hu, Yang ; Yang, Baojun

Author_Institution

Coll. of Inf. & Electron., Zhejiang Sci-Tech Univ., Hangzhou, China

fYear

2009

fDate

2-3 May 2009

Firstpage

79

Lastpage

83

Abstract

For detecting rice disease early and accurately, we presented an application of image processing techniques and Support Vector Machine (SVM) for detecting rice diseases. Rice disease spots were segmented and their shape and texture features were extracted. The SVM method was employed to classify rice bacterial leaf blight, rice sheath blight and rice blast. The results showed that SVM could effectively detect and classify these disease spots to an accuracy of 97.2%.

Keywords

agriculture; crops; feature extraction; image colour analysis; image segmentation; object detection; support vector machines; color texture features; feature extraction; image processing techniques; image segmentation; rice bacterial leaf blight; rice blast; rice disease detection; rice sheath blight; shape features; support vector machine; Crops; Diseases; Feature extraction; Image processing; Image segmentation; Microorganisms; Neural networks; Shape; Support vector machine classification; Support vector machines; image processing; rice diseases spots; support vector machine; texture features;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering Computation, 2009. ICEC '09. International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-0-7695-3655-2

Type

conf

DOI

10.1109/ICEC.2009.73

Filename

5167096