Leaf identification of sesame varieties using artificial neural networks (MLP and Neuro-Fuzzy)
Paper Details
Leaf identification of sesame varieties using artificial neural networks (MLP and Neuro-Fuzzy)
Abstract
This study focused on the identification of sesame leaf varieties using two artificial neural networks. Artificial neural network (ANN) is one of the efficient ways for solving complex problems such as identification tasks. This research was done in Islamic Azad University, Shahr-e-Rey Branch, during 2011 on 7 main sesame leaf varieties (Darab14, Dashtestan, Karaj1, Naz, Oltan, Varamin and Yekta) were grown in Varamin region of Iran. Different types of features (morphological, color, shape and chlorophyll) were extracted from color images using various methods. A multi layer perceptron (MLP) and Neuro-Fuzzy neural network were applied to classify leaf varieties. The MLP topological structure consisted of 42 input neurons, 7 output neurons and two hidden layers. The applied Neuro-fuzzy classifier had input and output layers as MLP and 60 rules instead of hidden layers. The identification accuracies computed 88.43% and 87.34% by MLP and Neuro-Fuzzy classifiers consequently, so the MLP classifier had better performance for classifying sesame leaf varieties.
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