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Table 3 Summary of selected references applying hyperspectral imaging to seed quality defect detection

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
Mung bean 900–1700 (1000–1600) 1 variety, 8 treatments 2400 Spectra and image PCA Reflectance OWb (single kernels) LDA, QDA Insect damage detection > 82% Kaliramesh et al. [38]
Soybean 900–1700 with soft x-ray 1 variety, 5 treatments 1000 Spectra and image GLCM Reflectance OW (single kernels) LDA, QDA Insect damage detection 99% (QDA) Chelladurai et al. [39]
Wheat 700–1100 1 variety, 4 insect varieties 1500 Spectra and image STEPDISC, GLCM, GLRM, PCA Reflectance OW (single kernels) LDA, QDA Insect damage detection 95.3–99.3% Singh et al. [37]
Wheat 400–1000 (450–920) 1 variety, 3 treatments 144 Spectra and image PCA Reflectance PWc prediction map and OW (single kernels) Spectral index Seed sprouted detection > 90% Xing et al. [36]
  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. bOW means objective-wise analysis, which means the analysis on ROIs (ROI can be bulk, single kernel or self-defined)
  3. cPW means pixel-wise analysis, which is the analysis on the pixels