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Estimating Quality of Canola Seed Using a Flatbed Scanner

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2007 ASAE Annual Meeting  073030.(doi:10.13031/2013.22945)
Authors:   Geetika Dilawari, Carol Jones
Keywords:   Canola grading, flat bed scanners

Various machine vision techniques have been applied to grade, size and classify various grain types like wheat, rice, lentils, pulses and soybeans. Little work has been done to grade canola using machine vision. 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 has been outlined as the main objective for this study. Samples with 0%, 2%, 5%, 10%, 20%, 40% and 60% foreign materials were used. Mean intensity values of Red (R), Green (G) and Blue (B) domains of sample images were recorded and analysed using histogram and discriminant analysis. The results from the analysis showed that it was possible to categorize canola into pure and impure samples. It was found that samples were broadly classified into three groups.

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