Classification of Wheat Types by Artificial Neural Network
Journal Title: International Journal of Intelligent Systems and Applications in Engineering - Year 2016, Vol 4, Issue 1
Abstract
In this study, the types of wheat seeds are classified using present data with artificial neural network (ANN) approach. Seven inputs, one hidden layer with 10 neurons and one output has been used for the ANN in our system. All of these parameters were real-valued continuous. The wheat varieties, Kama, Rosa and Canadian, characterized by measurement of main grain geometric features obtained by X-ray technique, have been analyzed. Results indicate that the proposed method is expected to be an effective method for recognizing wheat varieties. These seven input parameters reaches the 10-neurons hidden layer of the network and they are processed and then classified with an output. The classification process of 210 units of data using ANN is determined to make a successful classification as much as the actual data set. The regression results of the classification process is quite high. It is determined that the training regression R is 0,9999, testing regression is 0,99785 and the validation regression is 0,9947, respectively. Based on these results, classification process using ANN has been seen to achieve outstanding success.
Authors and Affiliations
Ali YASAR| Computer Programming, Guneysinir Vocational School of Higher Education Selcuk University Guneysinir, Konya, 42190,Turkey, Esra KAYA| Faculty of Technology, Electrical and Electronics Engineering Selcuk University , Konya, 42031,Turkey, Ismail SARITAS| Faculty of Technology, Electrical and Electronics Engineering Selcuk University , Konya, 42031,Turkey
Keywords
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- EP ID EP791
- DOI 10.18201/ijisae.64198
- Views 417
- Downloads 23
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