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Table 1 Summary of selected references applying hyperspectral imaging to seed classification and seed grading

From: Hyperspectral imaging for seed quality and safety inspection: a review

Seed

Spectral rangea

Varieties

Sample numbers

Features

Signal mode

Data analysis strategies

Main application type

Classification result (highest accuracy)

References

Spectra/image

Extraction/selection methods

Analysis level

Classification/regression methods

Barley, wheat and sorghum

1000–2498

1 variety of each kind of grain

150 of each kind of grain

Spectra

PCA

Reflectance

PWb prediction map and OWc (single kernels)

–

Grain topography classification

–

Manley et al. [19]

Black bean

390–1050 (501–1000)

3

300

Spectra and image

SPA, PCA, GLCM

Reflectance

OW (single kernels)

PLS-DA, SVM

Variety classification

98.33% (PLS-DA)

Sun et al. [15]

Grape seed

897–1752 (914–1715)

3 varieties, two growth soil

56

Spectra

PCA

Reflectance

OW (single kernels), PW PCA and prediction map

GDA

Assess

Stage of maturation of grape seeds

> 95%

Rodríguez-Pulido et al. [22]

Grape seed

874–1734 (975–1646)

3

43,357

Spectra and image

PCA

Reflectance

OW (single kernels)

SVM

Variety classification

94.30%

Zhao et al. [26]

Maize

874–1734 (972–1642)

2 (transgenic and non-transgenic)

2100

Spectra

PCA, CARS

Reflectance

PW PCA and prediction map, OW (single kernels)

PLS-DA, SVM

Transgenic and non-transgenic classification

99.5% (PLS-DA)

Feng et al. [24]

Maize

400–1000

4 varieties, 3 crop years

3600

Spectra

no

Reflectance

OW (single kernels)

LS-SVM

Variety classification

91.50%

Guo et al. [12]

Maize

400–1000

4 varieties, 3 crop years

2000

Spectra

no

Reflectance

OW (single kernels)

LS-SVM

Variety classification

94.80%

He et al. [13]

Maize

400–1000

4 varieties, 3 crop years

2000

Spectra

no

Reflectance

OW (single kernels)

LS-SVM

Variety classification

94.40%

Huang et al. [11]

Maize

400–1000 (400–1000)

17

1632

Spectra and image

PCA, SPA, GLCM, MDS

Reflectance

OW (single kernels)

LS-SVM

Variety classification

94.40%

Huang et al. [17]

Maize

1000–2500

18

36

Spectra and image

PCA

Reflectance

OW (single kernels), PW PCA and prediction map

PLS-DA

Textural, vitreous, floury and the third type endosperm

85% (PLS-DA)

Manley et al. [20]

Maize

975–2570 (1101–2503)

3 hardness

115

Spectra and image

PCA

Reflectance

PW PCA and prediction map, OW (single kernels)

PLS-DA

Hardness classification

97% (PLS-DA)

Williams and Kucheryavskiy [18]

Maize

874–1734 (924–1657)

14

1120

Spectra

joint skewness-based wavelength selection

Reflectance

OW (single kernels)

LS-SVM

Variety classification

98.18%

Yang et al. [7]

Maize

874–1734 (975–1646)

3

12,900

Spectra and image

PCA

Reflectance

OW (single kernels)

SVM, RBFNN

Variety classification

93.85% (RBFNN)

Zhao et al. [25]

Maize

380–1030 (500–900)

6

330

Spectra and image

PCA, KPCA, GLCM

Reflectance

OW (bulk samples)

LS-SVM, BPNN, PCA, KPCs

Classes classification

98.89% (PCA-GLCM-LS-SVM)

Zhang et al. [78]

Rice

390–1050 (500–951)

4 origins

240

Spectra and image

PCA, GLCM

Reflectance

OW (single kernels)

SVM

Variety classification

91.67%

Sun et al. [16]

Rice

874–1734 (1039–1612)

4

225

Spectra

PLS-DA, PCA

Reflectance

PW PCA and OW (bulk samples)

KNN, PLS-DA, SIMCA, SVM, RF

Seed cultivars classification

100% (SIMCA, SVM, and RF)

Kong et al. [5]

Soybean, maize and rice

400–1000 (400–1000)

3 of each kind of seed

225 of each kind of seed

Spectra

neighborhood mutual information

Reflectance

OW (single kernels)

ELM, RF

Variety classification

100% (ELM)

Liu et al. [8]

Waxy corn

400–1000 (430–980)

4

600

Spectra and image

SPA, GLCM

Reflectance

OW (single kernels)

PLS-DA, SVM

Variety classification

98.2% (SVM)

Yang et al. [14]

Wheat

960–1700 (960–1700)

8

2400

Image

WT, STEPDISC, PCA

Reflectance

PW and OW (bulk samples)

BPNN, LDA, QDA

Classes classification

99.1% (LDA)

Choudhary et al. [79]

Wheat

960–1700 (960–1700)

8

2400

Spectra

STEPDISC

Reflectance

OW (bulk samples)

LDA, QDA, Standard BPNN, Wardnet BPNN

Variety classification

94–100% (LDA)

Mahesh et al. [6]

Wheat

960–1700 (960–1700)

5

2500

Spectra

STEPDISC

Reflectance

PW PCA and OW (bulk samples)

LDA, QDA

Classes classification

90–100% (LDA)

Mahesh et al. [9]

  1. aThe spectral range without brackets relates to the range acquisition of instrument, while the spectral range in brackets represents the spectral range for practical analysis
  2. bPW means pixel-wise analysis, which is the analysis on the pixels
  3. cOW means objective-wise analysis, which means the analysis on ROIs (ROI can be bulk, single kernel or self-defined)