Classification system for rain fed wheat grain cultivars using artificial neural network

Abstract

Artificial neural network (ANN) models have found wide applications, including prediction, classification, system modeling and image processing. Image analysis based on texture, morphology and color features of grains is essential for various applications as wheat grain industry and cultivation. In order to classify the rain fed wheat cultivars using artificial neural network with different neurons number of hidden layers, this study was done in Islamic Azad University, Shahr-e-Rey Branch, during 2010 on 6 main rain fed wheat cultivars grown in different environments of Iran. Firstly, data on 6 colors, 11 morphological features and 4 shape factors were extracted, then these candidated features fed Multilayer Perceptron (MLP) neural network. The topological structure of this MLP model consisted of 21 neurons in the input layer, 6 neurons (Sardari, Sardari 39, Zardak, Azar 2, ABR1 and Ohadi) in the output layer and two hidden layers with different neurons number (21-30-10-6, 21-30-20-6 and 21-30-30-6). Finally, accuracy average for classification of rain fed wheat grains cultivars computed 86.48% and after feature selection application with UTA algorithm increased to 87.22% in 21-30-20-6 structure. The results indicate that the combination of ANN, image analysis and the optimum model architecture 21-30-20-6 had excellent potential for cultivars classification.

Key words: Rain fed wheat, grain, artificial neural networks (ANNs),  multilayer perceptron (MLP), feature selection.