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.
Highlights
Wheat is one of the major staple foods all over the world because of its agronomical adaptability and ability of its flour to be made into various food materials
The results indicate that the combination of Artificial neural network (ANN), image analysis and the optimum model architecture 2130-20-6 had excellent potential for cultivars classification
Due to the identification of rain fed wheat grain (Triticum aestivum L.) cultivars using artificial neural network and investigated different neurons number in hidden layers before and after doing UTA algorithm, this study was done in Islamic Azad University, Shahr-eRey Branch during 2010 on 6 wheat cultivars (Sardari, Sardari 39, Zardak, Azar 2, ABR1 and Ohadi) which were grown in different environments of Iran to simulate variation on grain shape and sizes to cover the range of variations encountered in reality (Figure 1)
Summary
Wheat is one of the major staple foods all over the world because of its agronomical adaptability and ability of its flour to be made into various food materials. In the case of crops such as wheat, where end use depends on use of a specific variety, identification of that variety is crucial. Variety identification is important for plant breeders and geneticists. The morphological characters of grains are heritable in nature (Harper et al, 1970) and play an important role in variety identification (Shouche et al, 2001).
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