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Table 5 Summary of selected references applying hyperspectral imaging to seed cleanness

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]
  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