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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

Hyperspectral images of four maize seed varieties: (a) HANG; (b) SU; (c) HU; (d) YAN.
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sensors-15-15578-f002: Hyperspectral images of four maize seed varieties: (a) HANG; (b) SU; (c) HU; (d) YAN.

Mentions: The dry seeds of waxy corn cultivars, Hangyunuo No.1 (HANG), Suyunuo 14 (SU), Huyunuo No.1 (HU), and Yanhejin 2000 (YAN) were used for all the experiments in this study. These four white corn cultivars were all hybrid corn and used as fresh corn. The growing periods of these four cultivars varied from 75 days to 85 days. Therefore,their optimal harvest time is various as fresh foods. These seeds were all produced in 2011 in China’s Zhejiang province,thus eliminating the effect of seed age and plant region. After being harvested and dried, the seeds were put in plastic bags and sealed in a plastic box to prevent moisture absorption during store. Before acquisition of the HSI data, the moisture content had been tested to make sure that all the samples had nearly the same moisture content. The final moisture content was 12% before signal acquisition. To explore the feasibility of maize seed cultivar classification using HSI, 150 samples of each variety were selected for imaging. The maize seeds were placed on a black painted platform where HSIs were captured. Considering the imparity of corn seeds, both sides of every seed were explored (Figure 2).


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)

Hyperspectral images of four maize seed varieties: (a) HANG; (b) SU; (c) HU; (d) YAN.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15578-f002: Hyperspectral images of four maize seed varieties: (a) HANG; (b) SU; (c) HU; (d) YAN.
Mentions: The dry seeds of waxy corn cultivars, Hangyunuo No.1 (HANG), Suyunuo 14 (SU), Huyunuo No.1 (HU), and Yanhejin 2000 (YAN) were used for all the experiments in this study. These four white corn cultivars were all hybrid corn and used as fresh corn. The growing periods of these four cultivars varied from 75 days to 85 days. Therefore,their optimal harvest time is various as fresh foods. These seeds were all produced in 2011 in China’s Zhejiang province,thus eliminating the effect of seed age and plant region. After being harvested and dried, the seeds were put in plastic bags and sealed in a plastic box to prevent moisture absorption during store. Before acquisition of the HSI data, the moisture content had been tested to make sure that all the samples had nearly the same moisture content. The final moisture content was 12% before signal acquisition. To explore the feasibility of maize seed cultivar classification using HSI, 150 samples of each variety were selected for imaging. The maize seeds were placed on a black painted platform where HSIs were captured. Considering the imparity of corn seeds, both sides of every seed were explored (Figure 2).

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