A Comparative Classification of Wheat Grains for Artificial Neural Network and Extreme Learning Machine

Yıl 2017, Cilt: 1 Sayı: 1, 14 - 21, 27.12.2017

Muhammet Fatih Aslan Kadir Sabancı Enes Yiğit Ahmet Kayabaşı Abdurrahim Toktaş Hüseyin Duysak

Öz

In this study, classification of two types of wheat grains
into bread and durum was carried out. The species of wheat grains in this
dataset are bread and durum and these species have equal samples in the dataset
as 100 instances. Seven features, including width, height, area, perimeter,
roundness, width and perimeter/area were extracted from each wheat grains. Classification
was separately conducted by Artificial Neural Network (ANN) and Extreme Learning Machine (ELM)
artificial intelligence techniques. Then the performances of models are
compared each other. The accuracy of testing was calculated 97.89% and 96.79%
for ANN and ELM, respectively. 

Anahtar Kelimeler

Classification, Wheat Grains, Artificial Neural Network, Extreme Learning Machine

Kaynakça

• [1] A. Taner, A. Tekgüler, H. Sauk, 2015. Classification of durum wheat varieties by artificial neural networks. Anadolu Tarım Bilimleri Dergisi, 30 (1) 51-59.
• [2] Anonymous, 2008. http://www.fao.org.
• [3] K. Sabanci, A. Toktas, and A. Kayabasi, 2017. Grain classifier with computer vision using adaptive neuro-fuzzy inference system. Journal of the Science of Food and Agriculture, doi:10.1002/jsfa.8264.
• [4] A. Pourreza, H. Pourreza, M. H. Abbaspour-Fard, H. Sadrnia, 2012. Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Computers and Electronics in Agriculture, 83 102-108.
• [5] M. Olgun, A. O. Onarcan, K. Ozkan, S. Isik, O. Sezer, K. Ozgisi, N. G. Ayter, Z. B. Basciftci, M. Ardic, O. Koyuncu, 2016. Wheat grain classification by using dense SIFT features with SVM classifier. Computers and Electronics in Agriculture, 122 185-190.
• [6] A. Babalik, F. M. Botsali, 2010. Yapay Sinir Ağı ve Görüntü İşleme Teknikleri Kullanarak Durum Buğdayının Camsılığının Belirlenmesi. Selçuk-Teknik Dergisi, 163-174.
• [7] F. Guevara-Hernandez, J. Gomez-Gil, 2011. A machine vision system for classification of wheat and barley grain kernels. Spanish Journal of Agricultural Research, 9 (3) 672-680.
• [8] A. R. Pazoki, F. Farokhi, Z. Pazoki, 2014. Classification of rice grain varieties using two artificial neural networks (MLP and Neuro-Fuzzy). The Journal of Animal & Plant Sciences, 24 (1) 336-343.
• [9] N. A. Abdullah and A. M. Quteishat, 2015. Wheat Seeds Classification using Multi-Layer Perceptron Artificial Neural Network. International Journal of Electronics Communication and Computer Engineering, 6 (2) 306-309.
• [10] A. Yasar, E. Kaya, and I. Sarıtas, 2016. Classification of Wheat Types by Artificial Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 4 12-15.
• [11] A. Bagheri and Y. Nikparast 2017. Seed Identification by Artificial Vision: A Review. Biological, Environmental and Agricultural Sciences, 1 (1) 28-32.
• [12] N. Otsu, 1979. A threshold selection method from grey-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9 (1) 62-66.
• [13] W.M. Hafizah and E. Supriyanto 2012. Automatic generation of region of interest for kidney ultrasound images using texture analysis. International Journal of Biology and Biomedical Engineering, 6 (1) 26-34.
• [14] G. B. Huang, Q. Y. Zhu and C. K. Siew, 2004. Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks. IEEE, 985-990.