An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains

Ahmet Kayabasi


Artificial Neural Networks (ANNs) have emerged as an important tool for classification problem. This paper presents an application of ANN model trained by artificial bee colony (ABC) optimization algorithm for classification the wheat grains into bread and durum. ABC algorithm is used to optimize the weights and biases of three-layer multilayer perceptron (MLP) based ANN. The classification is carried out through data of wheat grains (#200) acquired using image-processing techniques (IPTs). The data set includes five grain’s geometric parameters: length, width, area, perimeter and fullness. The ANN-ABC model input with the geometric parameters are trained through 170 wheat grain data and their accuracies are tested via 30 data. The ANN-ABC model numerically calculate the outputs with mean absolute error (MAE) of 0.0034 and classify the grains with accuracy of 100% for the testing process. The results of ANN-ABC model are compared with other ANN models trained by 4 different learning algorithms. These results point out that the ANN trained by ABC optimization algorithm can be successfully applied to classification of wheat grains. 


Classification;wheat grains;, image processing technique;artificial neural network;artificial bee colony algorithm

Full Text:

Submitted: 2017-12-17 20:01:03
Published: 2018-03-29 15:53:52
Search for citations in Google Scholar
Related articles: Google Scholar


K. Mollazade, M. Omid and A. Arefi (2012). Comparing data mining classifiers for grading raisins based on visual features. Comput Electron Agr. 84 124–131.

C. Sungur and H. Ozkan (2015). A real time quality control application for animal production by image processing. J Sci Food Agr. 95 2850–2857.

X. Yu, K. Liu, D. Wu and Y. He (2012). Raisin quality classification using least squares support vector machine (LSSVM) based on combined color and texture features. Food Bioprocess Tech. 5 1552–1563.

B.G. Hu, RG. Gosine, LX. Cao and CW. de Silva (1998). Application of a fuzzy classification technique in computer grading of fish products. IEEE T Fuzzy Syst. 6 144–152.

Y. Al Ohali (2011). Computer vision based date fruit grading system: Design and implementation. Journal of King Saud University-Computer and Information Sciences. 23 29–36.

R.P. Gálvez, F.J.E. Carpio, E.M. Guadix and A. Guadix (2016). Artificial neural networks to model the production of blood protein hydrolysates for plant fertilisation. J Sci Food Agr. 96 207–214.

J. Pet'ka, J. Mocak, P. Farkaš, B. Balla and M. Kováč (2001). Classification of Slovak varietal white wines by volatile compounds. J Sci Food Agr. 81 533–1539.

M. Berman, P.M. Connor, L.B. Whitbourn, D.A. Coward B.G. Osborne and M.D. Southan (2007). Classification of sound and stained wheat grains using visible and near infrared hyperspectral image analysis. J Near Infrared Spec 15 351–358.

K.S. Jamuna, S. Karpagavalli, P. Revathi, S. Gokilavani and E. Madhiya (2010). Classification of Seed Cotton Yield Based on the Growth Stages of Cotton Crop Using Machine Learning Techniques. International Conference on Advances in Computer Engineering 20-2, Bangalore, Karnataka, India, 312–315.

F. Guevara-Hernandez and J. Gomez-Gi (2011). A machine vision system for classification of wheat and barley grain kernels. Span J Agric Res. 9 672–680.

P. Zapotoczny (2011). Discrimination of wheat grain varieties using image analysis: morphological features. Eur Food Res Technol. 233 769–779.

C.V. Di Anibal, I. Ruisánchez, M. Fernández, R. Forteza, V. Cerdà and M.P. Callao (2012). Standardization of UV–visible data in a food adulteration classification problem. Food Chem. 134 2326–2331.

J.S. Prakash, K.A. Vignesh, C. Ashok and R. Adithyan (2012). Multi class Support Vector Machines classifier for machine vision application. In Machine Vision and Image Processing (MVIP) 14-15; Taipei, Taiwan, 197–199.

A.R. Pazoki, F. Farokhi and Z. Pazoki (2014). Classification of rice grain varieties using two Artificial Neural Networks (MLP and Neuro-Fuzzy). J Anim and Plant Sci. 24 336–343.

R. Muñiz-Valencia, J.M. Jurado, S.G. Ceballos-Magaña, A. Alcázar and J. Hernández-Díaz (2014). Characterization of Mexican coffee according to mineral contents by means of multilayer perceptrons artificial neural networks. Journal of Food Composition and Analysis. 34 7–11.

E.M. De Oliveira, D.S. Leme, B.H.G. Barbosa, M.P. Rodarte and R.G.F.A. Pereira (2016). A computer vision system for coffee beans classification based on computational intelligence techniques. J Food Eng. 171 22–27.

K. Sabanci, A. Kayabasi and A. Toktas (2017). Computer vision-based method for classification of the wheat grains using artificial neural network. Journal of the Science of Food and Agriculture. 97(8) 2588–2593.

K. Sabanci, A. Toktas and A. Kayabasi (2017). Grain classifier using computer vision with using adaptive neuro-fuzzy inference system. Journal of the Science of Food and Agriculture. 97(12) 3994–4000.

M.F. Aslan, K. Sabanci and A.Durdu (2017). Different Wheat Species Classifier Application of ANN and ELM. Journal of Multidisciplinary Engineering Science and Technology. 4(9) 8194-8198.

J.-R. Zhang, J. Zhang, T.M. Lok and M.R. Lyu (2007). A hybrid particle swarm optimization back-propagation algorithm for feedforward neural network training. Appl. Math. Comput. 185(2) 1026–1037.

H. Shah, R. Ghazali, N.M. Nawi Using an artificial bee colony algorithm for MLP training on earthquake time series data prediction, arXiv preprint, arXiv:1112.4628

E. Valian, S. Mohanna, S. Tavakoli (2011). Improved cuckoo search algorithm for feedforward neural network training. Int. J. Artif. Intell. Appl. 2 (3) 36–43.

S. Yu, K. Wang, Y.-M. Wei (2015). A hybrid self-adaptive particle swarm optimization genetic algorithm-radial basis function model for annual electricity demand prediction. Energy Convers. Manag. 91 176–185.

J. Wu, J. Long and M. Liu (2015). Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing. 148 136–142.

D. Karaboga (2005). An idea based on honey bee swarm for numerical optimization. Techn. Rep. TR06, Erciyes Univ. Press, Erciyes.

D. Karaboga, B. Gorkemli, C. Ozturk and N. Karaboga (2014). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42 (1) 21–57.

M. Zandieh, A. Azadeh, B. Hadadi and M. Saberi (2009). Application of neural networks for airline number of passenger estimation in time series state. J. Appl. Sci. 9(6), 1001–1013.

D. Karaboga, B. Akay and C. Ozturk (2007). Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. In: Modeling decisions for artificial intelligence. 318{329. Springer (2007)

N. Otsu (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE T Syst Man Cyb. 9 62–66.

S.Haykin (1994). Neural networks: A comprehensive foundation, Macmillan College Publishing Company, New York, A.B.D.

Abstract views:


Copyright (c) 2018 International Journal of Intelligent Systems and Applications in Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
© AtScience 2013-2018     -     AtScience is a registered trademark property of AtScience.