SEED CLASSIFICATION USING MACHINE VISION

This paper reports the results of applying digital image analysis in c onjunction with statistical pattern recognition to measure the size an d shape features of various seed types and to classify them into the p rimary grain, small seed, and large seed categories. The seed types us ed in each category were: hard red spring (HRS) wheat and barley as pr imary grains; canola, brown mustard, yellow mustard, oriental mustard, and flaxseed as small seeds; and 'Laird' lentils,'Eston' lentils, pea beans, green peas, black beans, and buckwheat as large seeds. The obj ective of the study was to assess the classification success in identi fying HRS wheat and barley from other small and large seeds using morp hological features. Orientation of the kernels for camera viewing was random. The kernels were, however, positioned manually in a non-touchi ng manner. Hard red spring wheat and barley were correctly identified from all other seed types with more than 99% accuracy. Small and large seed categories were successfully discriminated from each other. With in each of the small and large seed groups, however, the classificatio n was poor with up to 54.7% misclassification in small seed group and up to 30.3% misclassification in the large seed group. Canola yielded the worst classification with only 45.3% of canola seeds correctly dis criminated from other small seeds.