1. Muhammad A. Shahin and Stephen J. Symons
Design of a Multispectral Imaging System for Detecting Mildew Damage on Wheat Kernels
Muhammad A. Shahin and Stephen J. Symons
© Canadian Grain Commission, 2007

2. Mildew Damage
Physical condition of grain is the most important factor determining its value
Mildew damage is a serious degrading factor especially in eastern wheat classes
Grey discolouration of the kernel
Negative impact on milling quality of wheat
Darker flour

3. Mildew on Wheat Kernels

4. Mildew Assessment Visual inspection is the current method
Subjective and inconsistent
Objective instrumental methods are required to meet the needs of grain industry

5. Hyper-Spectral Imaging Explorations
65 samples of CESRW
3 grades; 3 levels in each grade
Scanned in bulk
HSI system nm
PLS regression model
image mean and image standard deviation spectra
correlated well with visual scores (R2 ~ 0.9)
HSI system has little practical value

6. Objectives
To select a set of 3-5 important wavelengths for predicting mildew levels in wheat samples without compromising the performance
To investigate through simulation if the selected wavelengths can be packaged as a low cost multispectral imaging system preserving spectral characteristics

7. Procedures
Regression coefficients of the PLS model were investigated for important wavelengths
PLS model with selected wavelengths was developed and compared with original model
Computer simulations of MSI system
band-pass filters to allow selected wavelengths

8. Wavelength Selection
450
561
917
861
811

9. PLS Regression PLS model input data Calibration Validation R2 RMSE
PLS model input data
Calibration
Validation R2
RMSE
Spectra over the entire wavelength range ( nm)
0.894
0.67
0.874
0.73
Spectra at 4 selected wavelengths
0.890
0.68
0.871
0.74

10. Filter Simulations

11. Simulation Results

12. Hardware Implementation
Options
Tuneable filters (LCTF; AOTF)
Numerous wavelength selections
Fast
Expensive
Band-pass filters in a motorized wheel
Limited filter slots
Slower
Less expensive

13. Conclusions
Mildew levels in wheat can be predicted with a PLS model using 4 wavelengths
Simulated spectral data at selected wavelengths matched the target hyperspectral data within less than 1%
Low cost hardware implementation can be achieved with band-pass filters in a motorized wheel

14. Thank you
Questions / comments?