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] |