Paper title:

Effect of Foreign Bodies on Recognition and Classification of Bulk Food Grains Image Samples

Published in: Issue 2, (Vol. 3) / 2009
Publishing date: 2009-10-20
Pages: 77-83
Author(s): Anami S. Basavaraj , Savakar G. Dayanand
Abstract. This paper presents an effect of Foreign Bodies (FB) on accuracies of recognition and classification of bulk food grain image samples using a Neural Network Approach. Any matter other than major food grains is considered as a foreign body in this work, such as stones, soil lumps, plant leaves, pieces of stems, weed, other types of grains etc. The amount of foreign bodies decides the quality of the food grains and hence it is necessary to determine the amount of foreign body present in food grains automatically to help farmers in sowing and marketing. Different food grains like, Green gram, Groundnut, Jowar, Rice and Wheat are considered in the study. The color and texture features are presented to the neural network for training and later of the unknown grain types mixed with foreign bodies. The combination of both color and texture features is employed in the work. The study reveals that the presence of even 10 percent of foreign bodies within food grain image samples reduces its recognition and classification accuracies as low as 60%. When the foreign body percentage is greater than 50, it becomes difficult to recognize and classify food grain image samples.
Keywords: Foreign Bodies, Feature Extraction, Foodgrain Samples, Neural Networks.
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