Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis
Abstract
:1. Introduction
2. Methods
2.1. Sample Preparation
2.2. SWIR Hyperspectral Imaging System
2.3. Image Acquisition and Correction
2.4. Data Extraction and Preprocessing
2.5. Partial Least-Squares Discriminant Analysis (PLS-DA)
2.6. Variable Importance in Projection (VIP)
2.7. Image Processing
2.8. Germination Test
3. Results and Discussion
3.1. Spectral Characteristics of Soybean Seeds
3.2. PLS-DA Classification Using Entire Wavelengths
3.3. PLS-DA Classification Using VIP Selected Variables
3.4. Kernel-Based Classification of Viable and Nonviable Soybean Seeds
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Preprocessing | Latent Variables | RMSECV |
---|---|---|
Raw | 14 | 0.312 |
SNV | 12 | 0.304 |
Max | 14 | 0.310 |
Mean | 12 | 0.322 |
Range | 14 | 0.303 |
Smoothing | 17 | 0.312 |
Calibration (n = 149,884) | Raw | SNV | Max | Mean | Range | Smoothing |
---|---|---|---|---|---|---|
Viable | 91.0 | 91.4 | 91.2 | 90.7 | 91.4 | 91.0 |
Non-viable | 92.8 | 92.7 | 92.8 | 93.1 | 92.7 | 92.8 |
Total | 91.9 | 92.1 | 92.0 | 91.9 | 92.1 | 91.9 |
Validation (n = 50,336) | ||||||
Viable | 89.0 | 89.4 | 89.1 | 88.6 | 89.4 | 89.0 |
Non-viable | 94.6 | 94.8 | 94.9 | 95.1 | 94.7 | 94.6 |
Total | 91.8 | 92.1 | 92.0 | 91.8 | 92.1 | 91.8 |
Calibration (n = 149,884) | Raw | SNV | Max | Mean | Range | Smoothing |
---|---|---|---|---|---|---|
Viable | 82.8 | 87.1 | 83.2 | 84.9 | 80.2 | 85.9 |
Non-viable | 84.7 | 88.8 | 86.5 | 89.2 | 79.5 | 88.1 |
Total | 83.7 | 88.0 | 84.9 | 87.1 | 79.9 | 87.0 |
Validation (n = 50,336) | ||||||
Viable | 80.8 | 84.1 | 80.0 | 81.0 | 76.7 | 85.6 |
Non-viable | 82.7 | 91.1 | 88.3 | 91.0 | 81.8 | 87.3 |
Total | 81.8 | 87.6 | 84.1 | 86.1 | 79.3 | 84.5 |
PLS-DA with Full Wavelengths | Optimum Detection Rate (%) | AUC | Calibration (n = 300) | Validation (n = 100) | ||
---|---|---|---|---|---|---|
Viable Accuracy (%) | Non-Viable Accuracy (%) | Viable Accuracy (%) | Non-Viable Accuracy (%) | |||
Raw | 71.3 | 0.9999 | 100 | 99.3 | 96.0 | 100 |
SNV | 63.4 | 0.9999 | 100 | 99.3 | 98.0 | 100 |
Max | 56.3 | 0.9999 | 100 | 99.3 | 98.0 | 100 |
Mean | 49.4 | 0.9998 | 100 | 99.3 | 98.0 | 100 |
Range | 63.0 | 0.9999 | 100 | 99.3 | 98.0 | 100 |
Smoothing | 70.9 | 0.9999 | 100 | 99.3 | 98.0 | 100 |
PLS-DA with VIP | ||||||
Raw | 52.0 | 0.9947 | 95.3 | 97.3 | 96.0 | 96.0 |
SNV | 33.9 | 0.9992 | 100 | 98.0 | 96.0 | 98.0 |
Max | 56.0 | 0.9941 | 94.7 | 97.3 | 96.0 | 98.0 |
Mean | 43.1 | 0.9959 | 95.3 | 98.0 | 96.0 | 98.0 |
Range | 49.7 | 0.9717 | 94.0 | 90.0 | 96.0 | 94.0 |
Smoothing | 55.7 | 0.9996 | 99.3 | 98.7 | 98.0 | 100 |
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Baek, I.; Kusumaningrum, D.; Kandpal, L.M.; Lohumi, S.; Mo, C.; Kim, M.S.; Cho, B.-K. Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis. Sensors 2019, 19, 271. https://doi.org/10.3390/s19020271
Baek I, Kusumaningrum D, Kandpal LM, Lohumi S, Mo C, Kim MS, Cho B-K. Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis. Sensors. 2019; 19(2):271. https://doi.org/10.3390/s19020271
Chicago/Turabian StyleBaek, Insuck, Dewi Kusumaningrum, Lalit Mohan Kandpal, Santosh Lohumi, Changyeun Mo, Moon S. Kim, and Byoung-Kwan Cho. 2019. "Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis" Sensors 19, no. 2: 271. https://doi.org/10.3390/s19020271
APA StyleBaek, I., Kusumaningrum, D., Kandpal, L. M., Lohumi, S., Mo, C., Kim, M. S., & Cho, B. -K. (2019). Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis. Sensors, 19(2), 271. https://doi.org/10.3390/s19020271