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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