Evaluation of Statistical and Neural Network Architectures for the Classification of Paddy Kernels Using Morphological Features

Abstract

In this study, a computer vision system was developed for the identification of paddy varieties based on the morphological features. Artificial neural networks and linear discriminant analysis methods were utilized for identification of the paddy seeds. Seven varieties of paddy (Tarom Hashemi, Tarom Molaei, Fajr, Neda, Kados, Sahel, and Shiroudi) were used in this research. The results showed that the identification with an artificial neural network classifier achieved over 91.5% prediction accuracy. It was evident that the predictive accuracy of artificial neural network model was higher than that evaluated with the linear discriminant analysis method. The values of error obtained from artificial neural network analysis ranged from 1.5 to 32.0% according to number of morphological features analyzed.