The performances of six color models and their features are compared for classification of Karjat-6, Karjat-2, Ratnagiri-4 and Ratnagiri-24 the four paddy varieties. Total of 15 color features-mean, standard deviation, variance, skewness and kurtosis for each channel are extracted from the high-resolution images of kernels and used as input features for classification. Different feature models consisting of the combination of the above features (MSVSK and MSV) are tested for their ability to classify these cereal grains. Effect of using different features on the accuracy of classification is studied. The most suitable feature from the feature set for accurate classification is identified. The accuracy percentage for YCbCr-MSVSK1 is 71.2 % and YCbCr –MSV1 is 65.4%. The MSVSK feature set outperformed the MSV feature set in most of the instances of classification. Similarly YCbCr color model performed well as compared to rest of the color models.

Mean, Neural-Network, Standard-deviation, Skewness, Kurtosis and Variance

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