1. Estimating Quality of Canola Seed Using a Flatbed Scanner

2. Introduction Grading of Canola: – Visual inspection – Follows US standard guidelines Machine Vision techniques using CCD cameras and flat bed scanner have been used to grade, size and classify rice, wheat, pulses, soybeans and lentils. These techniques have not been so far applied to grade canola

3. Objective Grading canola into samples with less than 2% foreign material (pure sample) and samples with more than 2% foreign materials (impure sample) using flat bed scanners

4. Material and Methods Canola Samples: 0%,2%,5%,10%,20%,40% and 60% foreign material. Five sub samples of 45gm from each sample were used for further testing Image Acquisition: Color image flat bed scanner (CanoScan 8400, Canon USA Inc., Lake Success, NY).Each sample was scanned at 150 dpi Color Calibration: Kodak gray cards (Catalog No. E1527795, Eastman Kodak Company, 1999) Data Acquisition: Mean values, that is the average intensity values, of the red (R), green (G) and blue (B) domains were recorded using Adobe Photoshop Elements 2.0 image editing software

5. Figure 1 Red Histogram data Figure 2 Green Histogram data Figure 3 Blue Histogram data

6. Figure 4 Canonical plot obtained from discriminant analysis using RGB domain Table 1 Classification Table for different canola samples* classified using discriminant analysis Percent Impurities 0%2%5%10%20%40%60% 0%5000000 2%0212000 5%0023000 10%0012200 20%0102200 40%0000023 60%0010004 * Number of samples for each type = 5

7. Figure 5 Canonical plot obtained from discriminant analysis using only R-G domain

8. (a) (c) (b) (d) Figure 6 Images 2% (a), 5% (b), 10 % (c), and 20% (d) samples

9. (a) (b) Figure 7 Images 40% (a) and 60% (b) samples

10. Conclusions Histogram Analysis: R and G domains were able to distinguish between pure and impure samples better than the B domain Discriminant analysis: Categorized the samples broadly speaking into three different groups – Samples with 0% foreign material were significantly different – Samples with 2%, 5%, 10%, 20% foreign material – Samples with 40% and 60% foreign material Visual analysis: Justified the results obtained