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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

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. 

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

Öz

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.
Toplam 14 adet kaynakça vardır.

Ayrıntılar

Konular Gıda Mühendisliği, Ziraat Mühendisliği
Bölüm Makaleler
Yazarlar

Muhammet Fatih Aslan 0000-0001-7549-0137

Kadir Sabancı 0000-0003-0238-9606

Enes Yiğit 0000-0002-0960-5335

Ahmet Kayabaşı 0000-0002-9756-8756

Abdurrahim Toktaş 0000-0002-7687-9061

Hüseyin Duysak Bu kişi benim

Yayımlanma Tarihi 27 Aralık 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 1 Sayı: 1

Kaynak Göster

APA Aslan, M. F., Sabancı, K., Yiğit, E., Kayabaşı, A., vd. (2017). A Comparative Classification of Wheat Grains for Artificial Neural Network and Extreme Learning Machine. International Journal of Environmental Trends (IJENT), 1(1), 14-21.
AMA Aslan MF, Sabancı K, Yiğit E, Kayabaşı A, Toktaş A, Duysak H. A Comparative Classification of Wheat Grains for Artificial Neural Network and Extreme Learning Machine. IJENT. Aralık 2017;1(1):14-21.
Chicago Aslan, Muhammet Fatih, Kadir Sabancı, Enes Yiğit, Ahmet Kayabaşı, Abdurrahim Toktaş, ve Hüseyin Duysak. “A Comparative Classification of Wheat Grains for Artificial Neural Network and Extreme Learning Machine”. International Journal of Environmental Trends (IJENT) 1, sy. 1 (Aralık 2017): 14-21.
EndNote Aslan MF, Sabancı K, Yiğit E, Kayabaşı A, Toktaş A, Duysak H (01 Aralık 2017) A Comparative Classification of Wheat Grains for Artificial Neural Network and Extreme Learning Machine. International Journal of Environmental Trends (IJENT) 1 1 14–21.
IEEE M. F. Aslan, K. Sabancı, E. Yiğit, A. Kayabaşı, A. Toktaş, ve H. Duysak, “A Comparative Classification of Wheat Grains for Artificial Neural Network and Extreme Learning Machine”, IJENT, c. 1, sy. 1, ss. 14–21, 2017.
ISNAD Aslan, Muhammet Fatih vd. “A Comparative Classification of Wheat Grains for Artificial Neural Network and Extreme Learning Machine”. International Journal of Environmental Trends (IJENT) 1/1 (Aralık 2017), 14-21.
JAMA Aslan MF, Sabancı K, Yiğit E, Kayabaşı A, Toktaş A, Duysak H. A Comparative Classification of Wheat Grains for Artificial Neural Network and Extreme Learning Machine. IJENT. 2017;1:14–21.
MLA Aslan, Muhammet Fatih vd. “A Comparative Classification of Wheat Grains for Artificial Neural Network and Extreme Learning Machine”. International Journal of Environmental Trends (IJENT), c. 1, sy. 1, 2017, ss. 14-21.
Vancouver Aslan MF, Sabancı K, Yiğit E, Kayabaşı A, Toktaş A, Duysak H. A Comparative Classification of Wheat Grains for Artificial Neural Network and Extreme Learning Machine. IJENT. 2017;1(1):14-21.

Environmental Engineering, Environmental Sustainability and Development, Industrial Waste Issues and Management, Global warming and Climate Change, Environmental Law, Environmental Developments and Legislation, Environmental Protection, Biotechnology and Environment, Fossil Fuels and Renewable Energy, Chemical Engineering, Civil Engineering, Geological Engineering, Mining Engineering, Agriculture Engineering, Biology, Chemistry, Physics,