RECOGNITION OF SEEDS USING NOVEL COLOR FEATURES FOR QUALITY SEED SELECTION

A new feature set based on color was developed to detect paddy seeds. This method has been applied to evaluate the power of various feature models based on six color feature sets. Images of all seeds were acquired with an in-house imaging system and processed with neural networks. The features applied as input for classification are the basic color features, and the new features derived from these basic features. These features were employed to establish the detection model for four paddy varieties viz. Karjat-6, Karjat-2, Ratnagiri-4 and Ratnagiri-24. The most satisfactory feature from the color features was identified for accurate classification. Performance of various feature models was investigated. Features based on method 3 were found poor, and method 6, where the normalized values are subtracted from all the three bands, showed better performance. The performance was in majority for most of the instances of classification. This method 6 called as proposed-color2 achieved an accuracy rate of 88.0%.