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Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification.

Yang X, Hong H, You Z, Cheng F - Sensors (Basel) (2015)

Bottom Line: To reduce the dimension of spectral data, the spectral feature vectors were constructed using the successive projections algorithm (SPA).The results demonstrate that combining spectral and appearance characteristic could obtain better classification results.This procedure has the potential for use as a new method for seed purity testing.

View Article: PubMed Central - PubMed

Affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China. feeling998@126.com.

ABSTRACT
The purity of waxy corn seed is a very important index of seed quality. A novel procedure for the classification of corn seed varieties was developed based on the combined spectral, morphological, and texture features extracted from visible and near-infrared (VIS/NIR) hyperspectral images. For the purpose of exploration and comparison, images of both sides of corn kernels (150 kernels of each variety) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing and derivation. To reduce the dimension of spectral data, the spectral feature vectors were constructed using the successive projections algorithm (SPA). Five morphological features (area, circularity, aspect ratio, roundness, and solidity) and eight texture features (energy, contrast, correlation, entropy, and their standard deviations) were extracted as appearance character from every corn kernel. Support vector machines (SVM) and a partial least squares-discriminant analysis (PLS-DA) model were employed to build the classification models for seed varieties classification based on different groups of features. The results demonstrate that combining spectral and appearance characteristic could obtain better classification results. The recognition accuracy achieved in the SVM model (98.2% and 96.3% for germ side and endosperm side, respectively) was more satisfactory than in the PLS-DA model. This procedure has the potential for use as a new method for seed purity testing.

No MeSH data available.


Related in: MedlinePlus

Weighted regression coefficients of the PLS-DA model with selected wavelengths. Spectra extracted from germ-down (a) and germ-up (b) images.
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sensors-15-15578-f007: Weighted regression coefficients of the PLS-DA model with selected wavelengths. Spectra extracted from germ-down (a) and germ-up (b) images.

Mentions: PLS-DA is another method employed for band selection. It is used to find the fundamental relations between the dependent variables (Y) and the independent variables (X). A latent variable approach is used to model the covariance structures in X and Y spaces. The number of latent variables were chosen based on the minimum root-mean-square error of cross validation (RMSECV) and it was found to be 11 latent variables. The regression coefficients of PLS-DA models, which were obtained from the spectra after SG preprocessing, are show in Figure 7. The wavelengths were selected as the optimal bands according to the highest absolute values of the regression coefficients.


Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification.

Yang X, Hong H, You Z, Cheng F - Sensors (Basel) (2015)

Weighted regression coefficients of the PLS-DA model with selected wavelengths. Spectra extracted from germ-down (a) and germ-up (b) images.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541845&req=5

sensors-15-15578-f007: Weighted regression coefficients of the PLS-DA model with selected wavelengths. Spectra extracted from germ-down (a) and germ-up (b) images.
Mentions: PLS-DA is another method employed for band selection. It is used to find the fundamental relations between the dependent variables (Y) and the independent variables (X). A latent variable approach is used to model the covariance structures in X and Y spaces. The number of latent variables were chosen based on the minimum root-mean-square error of cross validation (RMSECV) and it was found to be 11 latent variables. The regression coefficients of PLS-DA models, which were obtained from the spectra after SG preprocessing, are show in Figure 7. The wavelengths were selected as the optimal bands according to the highest absolute values of the regression coefficients.

Bottom Line: To reduce the dimension of spectral data, the spectral feature vectors were constructed using the successive projections algorithm (SPA).The results demonstrate that combining spectral and appearance characteristic could obtain better classification results.This procedure has the potential for use as a new method for seed purity testing.

View Article: PubMed Central - PubMed

Affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China. feeling998@126.com.

ABSTRACT
The purity of waxy corn seed is a very important index of seed quality. A novel procedure for the classification of corn seed varieties was developed based on the combined spectral, morphological, and texture features extracted from visible and near-infrared (VIS/NIR) hyperspectral images. For the purpose of exploration and comparison, images of both sides of corn kernels (150 kernels of each variety) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing and derivation. To reduce the dimension of spectral data, the spectral feature vectors were constructed using the successive projections algorithm (SPA). Five morphological features (area, circularity, aspect ratio, roundness, and solidity) and eight texture features (energy, contrast, correlation, entropy, and their standard deviations) were extracted as appearance character from every corn kernel. Support vector machines (SVM) and a partial least squares-discriminant analysis (PLS-DA) model were employed to build the classification models for seed varieties classification based on different groups of features. The results demonstrate that combining spectral and appearance characteristic could obtain better classification results. The recognition accuracy achieved in the SVM model (98.2% and 96.3% for germ side and endosperm side, respectively) was more satisfactory than in the PLS-DA model. This procedure has the potential for use as a new method for seed purity testing.

No MeSH data available.


Related in: MedlinePlus