期刊
SENSORS
卷 12, 期 12, 页码 17234-17246出版社
MDPI
DOI: 10.3390/s121217234
关键词
maize seed; variety identification; hyperspectral imaging; principal component analysis; kernel principal component analysis; gray-level co-occurrence; least squares-support vector machine; back propagation neural network
资金
- National Science and Technology Support Program of China [2011BAD20B12]
- Zhejiang Provincial Natural Science Foundation of China [Z3090295]
- Agricultural Science and Technology Achievements Transformation Fund Programs [2009GB23600517]
- Fundamental Research Funds for the Central Universities [2012FZA6005]
Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380-1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据