estimating quality of canola seed using a flatbed scanner
DESCRIPTION
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 scannersTRANSCRIPT
Estimating Quality of Canola Seed Using a Flatbed Scanner
IntroductionGrading 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
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
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
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Figure 1 Red Histogram data Figure 2 Green Histogram data
Figure 3 Blue Histogram data
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% 5 0 0 0 0 0 0
2% 0 2 1 2 0 0 0
5% 0 0 2 3 0 0 0
10% 0 0 1 2 2 0 0
20% 0 1 0 2 2 0 0
40% 0 0 0 0 0 2 3
60% 0 0 1 0 0 0 4
* Number of samples for each type = 5
Figure 5 Canonical plot obtained from discriminant analysis using only R-G domain
(a)
(c)
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(d)
Figure 6 Images 2% (a), 5% (b), 10 % (c), and 20% (d) samples
(a) (b)
Figure 7 Images 40% (a) and 60% (b) samples
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