Table 3 Performance of support vector machine (SVM) model in identifying single hard seed in six species
Species | Accuracy (%) | Sensitivity (%) | Specificity (%) |
|---|---|---|---|
Acacia seyal | |||
 Calibration | 89.29 | 100.00 | 0.00 |
 CV | 89.29 | 100.00 | 0.00 |
 Validation | 88.33 | 100.00 | 0.00 |
Galega orientulis | |||
 Calibration | 91.79 | 90.98 | 92.52 |
 CV | 89.29 | 87.22 | 91.16 |
 Validation | 87.50 | 88.00 | 87.14 |
Glycyrrhiza glabra | |||
 Calibration | 82.14 | 57.65 | 92.82 |
 CV | 67.50 | 4.71 | 94.87 |
 Validation | 80.00 | 46.88 | 92.05 |
Medicago sativa | |||
 Calibration | 95.00 | 93.75 | 96.32 |
 CV | 86.79 | 83.33 | 90.44 |
 Validation | 89.17 | 84.62 | 92.65 |
Melilotus officinalis | |||
 Calibration | 90.36 | 73.08 | 97.03 |
 CV | 88.57 | 71.79 | 95.05 |
 Validation | 91.67 | 76.67 | 96.67 |
Thermopsis lanceolata | |||
 Calibration | 80.71 | 20.00 | 99.07 |
 CV | 80.00 | 20.00 | 98.14 |
 Validation | 77.50 | 7.41 | 97.85 |