Support vector machine for recognition of cucumber leaf diseases

Author

Jian, Zhang ; Wei, Zhang

Author_Institution

Inst. of Intell. Machines, Chinese Acad. of Sci., Hefei, China

Volume

5

fYear

2010

fDate

27-29 March 2010

Firstpage

264

Lastpage

266

Abstract

Support vector machine (SVM) is discussed to use for recognizing cucumber leaf diseases in this paper. Considering that it is a small number of samples, a new experimental program has been proposed which takes each spot of leaves as a sample instead of taking each leaf as a sample. In the experiments Radial Basis Function (RBF), polynomial and Sigmoid kernel function were also used to carry out comparative tests. The results showed that, the SVM method based on RBF kernel function and taking each spot as a sample made the best performance for classification of cucumber leaf diseases.

Keywords

agricultural products; image classification; image recognition; polynomials; radial basis function networks; support vector machines; RBF kernel function; Sigmoid kernel function; cucumber leaf diseases classification; cucumber leaf diseases recognition; polynomial kernel function; radial basis function; support vector machine; Algorithm design and analysis; Crops; Diseases; Functional analysis; Image recognition; Kernel; Machine intelligence; Risk analysis; Support vector machine classification; Support vector machines; classification; cucumber leaf disease; kernel function; pattern recognition; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computer Control (ICACC), 2010 2nd International Conference on

Conference_Location

Shenyang

Print_ISBN

978-1-4244-5845-5

Type

conf

DOI

10.1109/ICACC.2010.5487242

Filename

5487242