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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

An example of spectra preprocessing. (a) Original spectrum; (b) spectrum after SG smoothing and derivation.
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sensors-15-15578-f004: An example of spectra preprocessing. (a) Original spectrum; (b) spectrum after SG smoothing and derivation.

Mentions: Data was preprocessed in order to highlight the differences among the study samples. The spectral sensibility of the CCD camera has lower signal-noise ratio near 400 nm and 1000 nm wavelength. Therefore, spectral information from 430 nm to 980 nm was chosen for further analysis. Before selecting optimal wavelength, the spectra were preprocessed by Savitzky-Golay (SG) smoothing filter and derivate. The role of the smoothing filter was to improve signal-noise ratio and eliminate the random noise. The derivate function was used to correct the baseline effects, which could amplify and resolve the overlapped signal. In SG smoothing, the frame size and the polynomial order must be specified. The frame size must be odd and set at 21, and the polynomial order must be less than the frame length and was set at 2 in this experiment. The first derivative was applied on the smoothed spectra by a SG filter. The smoothed and derivate spectra were employed in the following optimal wavelength selection (Figure 4).


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)

An example of spectra preprocessing. (a) Original spectrum; (b) spectrum after SG smoothing and derivation.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15578-f004: An example of spectra preprocessing. (a) Original spectrum; (b) spectrum after SG smoothing and derivation.
Mentions: Data was preprocessed in order to highlight the differences among the study samples. The spectral sensibility of the CCD camera has lower signal-noise ratio near 400 nm and 1000 nm wavelength. Therefore, spectral information from 430 nm to 980 nm was chosen for further analysis. Before selecting optimal wavelength, the spectra were preprocessed by Savitzky-Golay (SG) smoothing filter and derivate. The role of the smoothing filter was to improve signal-noise ratio and eliminate the random noise. The derivate function was used to correct the baseline effects, which could amplify and resolve the overlapped signal. In SG smoothing, the frame size and the polynomial order must be specified. The frame size must be odd and set at 21, and the polynomial order must be less than the frame length and was set at 2 in this experiment. The first derivative was applied on the smoothed spectra by a SG filter. The smoothed and derivate spectra were employed in the following optimal wavelength selection (Figure 4).

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