Abstract
The paper presents an artificial neural network based approach to corroborate the effect of variations in illumination, distance of image acquisition and size on recognition and classification accuracies of bulk food grain image samples. Different food grains samples like Wheat, Groundnut, Green gram and Jowar are considered. The image samples are taken by varying acquisition distances, illumination and sizes. The natural light source and the minimum distance of 40 centimeters are ideal for image acquisition compared to other light sources and varying distance of image acquisition. The experimental results have shown a reduction in accuracy in all the cases other than natural lighting condition and fixed distance of acquisition. The same is true with human vision system too. Variations in image sizes also affect the recognition and classification accuracies. Hence, it is inferred that any variation in the size of the image and the distance of acquisition has an impact on the accuracy of recognition of food grain samples.
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