1. Detection of Sprouted and Midge- Damaged Wheat kernels Using Near-Infrared Hyperspectral Imaging C.B. Singh, D.S. Jayas, J. Paliwal, N.D.G. White CSBE\ SCGAB Annual Conference Vancouver, BC July 13-16, 2008 07/16/08CSBE 2008 Conference

2. Brief overview 07/16/08CSBE 2008 Conference  Sprout and midge-damage  Current detection methods  Hyperspectral imaging  Calibration and pre-processing  Data reduction and feature selection  Classification results  Conclusion

3. Sprout damage  Sprouting is considered one of the important degrading factors of wheat in Canada  Results in decrease in starch, increase in sugar, dry matter loss, and poor bread making quality  Sprouted kernels are more vulnerable to insect infestation 07/16/08CSBE 2008 Conference

4. Midge-damage  Occurs when the orange wheat blossom midge larva feeds on the developing wheat kernels  Causes the kernels to shrivel, crack, and become deformed and misshapen  Splits the kernel’s pericarp, facilitating the water uptake and hence sprouting in poor weather 07/16/08CSBE 2008 Conference

5. 07/16/08CSBE 2008 Conference  Visual inspection  Falling number test  X-rays  NIR spectroscopy Detection methods

6. 07/16/08CSBE 2008 Conference  To identify the spectral bands which are appropriate for detection of sprout and midge-damage in wheat  To develop calibration algorithms to detect sprouted and midge-damaged wheat kernels Research objectives

7. Sample preparation  Artificially sprouted and midge-damaged wheat samples  Five locations: Camrose East, AB; Vegreville, AB; North Battleford, SK; Cutknife, SK; and Yorkton, SK  Midge-damaged kernels were manually selected 07/16/08CSBE 2008 Conference

8. Imaging system 07/16/08CSBE 2008 Conference Stand InGaAs camera LCTF Area of view Halogen-tungsten lamp Data storage and analysis

9. Calibration & pre-processing  Dark current measurement  Co-addition  Filtering and segmentation  Transformation into reflectance 07/16/08CSBE 2008 Conference

10. Hypercube Reflectance Wavelength x y 07/16/08CSBE 2008 Conference labeling (Singh et al. 2007)‏

11. 07/16/08CSBE 2008 Conference PC loadings

12. 07/16/08CSBE 2008 Conference Reflectance spectra

13. 07/16/08CSBE 2008 Conference First PC score images HealthySproutedMidge-damaged

14.  Wavelengths 1101.7, 1132.2, and 1305.1 nm were identified as significant wavelengths  Features: Mean, maximum, minimum, median, standard deviation, and variance  Discriminant analysis (linear, quadratic, and Mahalanobis) were used for classification 07/16/08CSBE 2008 Conference Classification model

15. 07/16/08CSBE 2008 Conference Classification results ClassifierClassification accuracy(%)‏ HealthySprouted Linear Quadratic Mahalanobis 100.0 98.3 100.0 Table: Classification of sprouted wheat kernels

16. 07/16/08CSBE 2008 Conference Classification results Actual classClassification accuracies by various discriminant classifiers (%)‏ LinearQuadraticMahalanobis Healthy96.795.085.0 Yorkton Camrose East Cutknife Vegreville North Battleford 86.7 100.0 96.7 83.3 100.0 93.3 100.0 86.7 100.0 96.7 100.0 91.7 100.0 Table: Midge-damaged kernels (Crease-down)‏

17. 07/16/08CSBE 2008 Conference Classification results Actual classClassification accuracies by various discriminant classifiers (%)‏ LinearQuadraticMahalanobis Healthy93.3100.065.0 Yorkton Camrose East Cutknife Vegreville North Battleford 91.7 100.0 68.3 100.0 81.7 100.0 55.0 100.0 98.3 100.0 95.0 100.0 Table: Midge-damaged kernels (Crease-up)‏

18.  Three wavelengths 1101.7, 1132.2, and 1305.1 nm were found as the most significant  Linear discriminant classifier classified more than 96% healthy kernels  Mahalanobis discriminant classifier correctly classified 91-100% damaged kernels 07/16/08CSBE 2008 Conference Conclusion

19. Mr. Ian Wise 07/16/08CSBE 2008 Conference Acknowledgements

20. 07/16/08CSBE 2008 Conference Thank You!