Automatic recognition of pulse crops using image processing
Keywords:Automatic recognition, Image processing, Machine vision, Pulse crops
The present study was explored the feasibility of implementing fast and reliable computer-based systems for the automatic recognition of pulse crops from color and gray intensity images. Pulse crops size, shape, color and texture characteristics are obtained by standard image-processing techniques and their discriminating power as classification features was assessed. These investigations were performed on a database containing 102 images of most common four pulse crops that were Lentil, Ground Nut, Chick-pea and Split-pea. Each image contains approximately 15-20 pulses of same and mix varieties together and considers the implementation of a simple RGB and gray color model for recognition. The results indicate that classifier based on an adequately selected set of classification features has an excellent performance. The success rates of Lentil, Ground Nut, Chick-pea and Split-pea were 90.02%, 90.33%, 91.96% and 83.58%, respectively. In addition, the recognition gave highest percentages using distinct characteristics as classification features.
Res. Agric., Livest. Fish.2(2): 215-220, August 2015
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