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, /16/08CSBE 2008 Conference

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

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

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

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

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

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

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

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

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

07/16/08CSBE 2008 Conference PC loadings

07/16/08CSBE 2008 Conference Reflectance spectra

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

 Wavelengths , , and 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

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

07/16/08CSBE 2008 Conference Classification results Actual classClassification accuracies by various discriminant classifiers (%)‏ LinearQuadraticMahalanobis Healthy Yorkton Camrose East Cutknife Vegreville North Battleford Table: Midge-damaged kernels (Crease-down)‏

07/16/08CSBE 2008 Conference Classification results Actual classClassification accuracies by various discriminant classifiers (%)‏ LinearQuadraticMahalanobis Healthy Yorkton Camrose East Cutknife Vegreville North Battleford Table: Midge-damaged kernels (Crease-up)‏

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

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

07/16/08CSBE 2008 Conference Thank You!