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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

Comparison of spectral reflectance of four maize seed cultivars extracted from germ-up (a) and germ-down (b) images.
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sensors-15-15578-f005: Comparison of spectral reflectance of four maize seed cultivars extracted from germ-up (a) and germ-down (b) images.

Mentions: The mean relative reflectance spectra of the four varieties of maize seeds are shown in Figure 5. Comparison results of the four spectra curves showed similar trends between different varieties. In more detail, the germ-up side spectra of HU and SU nearly overlapped at wavelength from 680 nm to 940 nm. However, for germ-down side, the spectra have obvious differences between HU and SU in this region. SU and HANG can be separated at wavelength region from 500 nm to 940 nm in germ-up side images. Meanwhile, SU and HANG also have some differences below 500 nm in germ-down side images. For these four varieties, the wavelength regions that can separate them were inconsistent between the spectra of germ-up and germ-down sides. In Figure 5a, the spectra of HU and SU nearly overlapped in the range of 680–1000 nm. However, in Figure 5b, the spectra of HU and SU were different. These differences may be related to different chromospheres and other components of both sides of maize kernels. For both sides of corn seed, it is necessary to analyze them separately.


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)

Comparison of spectral reflectance of four maize seed cultivars extracted from germ-up (a) and germ-down (b) images.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15578-f005: Comparison of spectral reflectance of four maize seed cultivars extracted from germ-up (a) and germ-down (b) images.
Mentions: The mean relative reflectance spectra of the four varieties of maize seeds are shown in Figure 5. Comparison results of the four spectra curves showed similar trends between different varieties. In more detail, the germ-up side spectra of HU and SU nearly overlapped at wavelength from 680 nm to 940 nm. However, for germ-down side, the spectra have obvious differences between HU and SU in this region. SU and HANG can be separated at wavelength region from 500 nm to 940 nm in germ-up side images. Meanwhile, SU and HANG also have some differences below 500 nm in germ-down side images. For these four varieties, the wavelength regions that can separate them were inconsistent between the spectra of germ-up and germ-down sides. In Figure 5a, the spectra of HU and SU nearly overlapped in the range of 680–1000 nm. However, in Figure 5b, the spectra of HU and SU were different. These differences may be related to different chromospheres and other components of both sides of maize kernels. For both sides of corn seed, it is necessary to analyze them separately.

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