Research on leaf species identification based on principal component and linear discriminant analysis

Abstract

A method of leaf species identification based on principal component and linear discriminant analysis is proposed in this paper. The method consists of four phases. Leaf images obtained by cameras or other devices typically have petioles and wormholes that affect the recognition rate. Hence, it is necessary to first preprocess the images before feature extraction. After preprocessing of the leaf samples, the binary image, grayscale image, and texture image of the leaves are output for feature extraction. Then, shape features and texture features are extracted to describe the leaves. The shape features can be divided into three groups: geometric characteristics, Hu moment invariants features, and structural characteristics. The texture features consists of gray level co-occurrence matrix features, fractal dimension features, Local Binary Patterns features, and Gabor features. Next, principal component analysis and linear discriminant analysis are combined to reduce the dimension of the features. Finally, Back Propagation Neural Network is used to classify the feature data. The proposed method was tested on two leaf image datasets: Flavia and ICL; the average accuracy of leaf species identification was 94.22 and 87.82%, respectively. The experiment demonstrated the effectiveness of the proposed method.