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Abstract
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Article Information:
Corn Seed Varieties Classification Based on Mixed Morphological and Color Features Using Artificial Neural Networks
Alireza Pazoki, Fardad Farokhi and Zohreh Pazoki
Corresponding Author: Alireza Pazoki
Submitted: October 03, 2012
Accepted: December 03, 2012
Published: October 20, 2013 |
Abstract:
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The ability of Multi-Layer Perceptron (MLP) and Neuro-Fuzzy neural networks to classify corn seed varieties based on mixed morphological and color Features has been evaluated that would be helpful for automation of corn handling. This research was done in Islamic Azad University, Shahr-e-Rey Branch, during 2011 on 5 main corn varieties were grown in different environments of Iran. A total of 12 color features, 11 morphological features and 4 shape factors were extracted from color images of each corn kernel. Two types of neural networks contained Multilayer Perceptron (MLP) and Neuro-Fuzzy were used to classify the corn seed varieties. Average classification’s accuracy of corn seed varieties were obtained 94% and 96% by MLP and Neuro-Fuzzy classifiers respectively. After feature selection by UTA algorithm, more effective features were selected to decrease the classification processing time, without any meaningful decreasing of accuracies.
Key words: Artificial Neural Networks (ANNs), corn, Feature selection, Multi layer perceptron (MLP), neuro-fuzzy, seed,
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Cite this Reference:
Alireza Pazoki, Fardad Farokhi and Zohreh Pazoki, . Corn Seed Varieties Classification Based on Mixed Morphological and Color Features Using Artificial Neural Networks. Research Journal of Applied Sciences, Engineering and Technology, (19): 3506-3513.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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