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 | ||||||||
Wheat | 960–1700 (1000–1600) | Extraneous materials (barley, canola, maize, flaxseed, oats, rye, and soybean), dockage types (broken wheat kernels, buckwheat, chaff, wheat spikelets, stones, and wild oats) and animal excreta types (deer and rabbit droppings) | 4800 | Spectra | No | Reflectance | OWb (single particles) | SVM, NB and KNN | Foreign materials detection | > 80% | Ravikanth et al. [66] |
Wheat, barley, corn | 314–975 (403–950) | 10 varieties, (material other than grain, such as chaff and straw) | More than 40,000 pixels | Spectra | GA-PLS-DA | Reflectance | PWc spectra, PW prediction map | GA-PLS-DA | Foreign materials detection | – | Wallays et al. [65] |