Title :
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
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;
Conference_Titel :
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-5845-5
DOI :
10.1109/ICACC.2010.5487242
