Abstract
Abstract The identification of seed varieties is of significance to ensure seed purity. In this study, 1,600 barley seeds (400 per variety) were selected as the samples. By using a near‐infrared hyperspectral imaging system, the hyperspectral cube data on the reverse and ventral sides of each barley seed were obtained. Three preprocessing methods (standard normal variate, multivariate scatter correction, and Savitzky–Golay first derivative) and three discriminant models (k‐nearest neighbors, support vector machine [SVM], and random forest) were selected for preprocessing and modeling, respectively. The results indicated that the reverse spectral data preprocessed by multivariate scatter correction and the ventral spectral data preprocessed by Savitzky–Golay first derivative had the best modeling results. In order to further simplify the model, feature selection algorithm (successive projections algorithm) was used for feature wavelength selection. After multivariate data analysis, the combination of k‐nearest neighbors and successive projections algorithm based on the reverse spectral data was selected as the best discriminant model. The accuracy of the calibration set and the prediction set were 95.52% and 93.71%, and the Kappa value were 0.9424 and 0.9156, respectively. The reverse spectral data of barley seeds of the validation set were used to verify the accuracy of the discriminant model and visualize the classification results. Finally, considering the practical application, the mixed spectral data containing different ratios of the reverse spectral data was analyzed, and satisfactory results were obtained. It can be concluded from this study that hyperspectral imaging technology was a very promising method for identification of barley seed varieties. Practical applications With the circulation of the seed market, the traditional methods used for variety identification can no longer meet the needs of large‐scale, high‐quality production. As a nondestructive determination method, hyperspectral imaging technology combined machine vision and near‐infrared spectroscopy. In this study, hyperspectral imaging technology was used to identify barley seed varieties, and satisfactory results were obtained. It can provide a theoretical reference for future multispectral online detection technology.
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Claims 8
Claim · model-written summary
Reverse spectra with multivariate scatter correction gave the best modeling results.
- Taxon
- barley seeds
.jpg?width=60)
Hordeum vulgaret0 - Outcome
- best modeling resultse0
- Comparator
- ventral spectral datae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
.jpg?width=500)
Verbatim
e0“the reverse spectral data preprocessed by multivariate scatter correction and the ventral spectral data preprocessed by Savitzky–Golay first derivative had the best modeling results”
t0“barley seeds varieties using hyperspectral data”
Claim · model-written summary
Ventral spectra with Savitzky–Golay first derivative gave the best modeling results.
- Taxon
- barley seedsHordeum vulgaret0
- Outcome
- best modeling resultse0
- Comparator
- reverse spectral datae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the reverse spectral data preprocessed by multivariate scatter correction and the ventral spectral data preprocessed by Savitzky–Golay first derivative had the best modeling results”
t0“barley seeds varieties using hyperspectral data”
Claim · model-written summary
K nearest neighbors with successive projections on reverse spectra was selected as the best model.
- Taxon
- barley seedsHordeum vulgaret0
- Outcome
- best discriminant modele0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the combination of k‐nearest neighbors and successive projections algorithm based on the reverse spectral data was selected as the best discriminant model”
t0“barley seeds varieties using hyperspectral data”
Claim · model-written summary
Calibration accuracy was 95.52%.
- Taxon
- barley seedsHordeum vulgaret0
- Outcome
- accuracy of the calibration sete0
- Value
- 95.52%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The accuracy of the calibration set and the prediction set were 95.52% and 93.71%”
t0“barley seeds varieties using hyperspectral data”
Claim · model-written summary
Prediction accuracy was 93.71%.
- Taxon
- barley seedsHordeum vulgaret0
- Outcome
- prediction sete0
- Value
- 93.71%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The accuracy of the calibration set and the prediction set were 95.52% and 93.71%”
t0“barley seeds varieties using hyperspectral data”
Claim · model-written summary
Calibration Kappa was 0.9424.
- Taxon
- barley seedsHordeum vulgaret0
- Outcome
- Kappa valuee0
- Value
- 0.9424e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the Kappa value were 0.9424 and 0.9156, respectively”
t0“barley seeds varieties using hyperspectral data”
Claim · model-written summary
Prediction Kappa was 0.9156.
- Taxon
- barley seedsHordeum vulgaret0
- Outcome
- Kappa valuee0
- Value
- 0.9156e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the Kappa value were 0.9424 and 0.9156, respectively”
t0“barley seeds varieties using hyperspectral data”
Claim · model-written summary
Mixed reverse and ventral spectral data gave satisfactory results.
- Taxon
- barley seedsHordeum vulgaret0
- Outcome
- satisfactory resultse0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the mixed spectral data containing different ratios of the reverse spectral data was analyzed, and satisfactory results were obtained”
t0“barley seeds varieties using hyperspectral data”