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Open AccessArticle

Classification of Varieties of Grain Species by Artificial Neural Networks

Department of Agricultural Machinery, Faculty of Agriculture, University of Ondokuz Mayıs, 55139 Samsun, Turkey
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Agronomy 2018, 8(7), 123; https://doi.org/10.3390/agronomy8070123
Received: 8 May 2018 / Revised: 6 July 2018 / Accepted: 13 July 2018 / Published: 18 July 2018
In this study, an Artificial Neural Network (ANN) model was developed in order to classify varieties belonging to grain species. Varieties of bread wheat, durum wheat, barley, oat and triticale were utilized. 11 physical properties of grains were determined for these varieties as follows: thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters. It was found that these properties had been statistically significant for the varieties. An Artificial Neural Network was developed for classifying varieties. The structure of the ANN model developed was designed to have 11 inputs, 2 hidden and 2 output layers. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour were used as input parameters; and species and varieties as output parameters. While classifying the varieties by the ANN model developed, R2, RMSE and mean error were found to be 0.99, 0.000624 and 0.009%, respectively. In classifying the species, these values were found to be 0.99, 0.000184 and 0.001%, respectively. It has shown that all the results obtained from the ANN model had been in accordance with the real data. View Full-Text
Keywords: artificial neural networks; physical properties; bread wheat; durum wheat; barley; oat; triticale artificial neural networks; physical properties; bread wheat; durum wheat; barley; oat; triticale
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Taner, A.; Öztekin, Y.B.; Tekgüler, A.; Sauk, H.; Duran, H. Classification of Varieties of Grain Species by Artificial Neural Networks. Agronomy 2018, 8, 123.

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