A method of plant classification based on wavelet transforms and support vector machines

Authors:
Jiandu Liu;Shanwen Zhang;Shengli Deng
Affiliations:
Missile Institute, Air-Force Engineering University, Sanyuan, P.R. China;Hefei Institute of Intelligent Machines, Chinese Academy of Science, Hefei, Anhui, P.R. China;Air-Force 93861 Army, Sanyuan, P.R. China
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

As one of the most important morphological taxonomy features, plant leaf with many strong points has significant influence on research. In this paper, we propose a novel method of plant classification from leaf image set based on wavelet transforms and support vector machines (SVMS). Firstly, the leaf images are converted into the time-frequency domain image by wavelet transforms without any further preprocessing such as image enhancement and texture thinning, and then feature extraction vector is conducted. Then the effectiveness of the proposed method is evaluated by the classification accuracy of SVM classifier. The experimental results about the data set with 300 leaf images show that the method has higher recognition rate and faster processing speed.