Journals / International Journal of Intelligent Systems and Applications in Engineering / 2018 / Cilt: 6 - Sayı: 1

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

Pages
85–91
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These results point out that the ANN trained by ABC optimization algorithm can be successfully applied to classification of wheat grains

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Artificial Neural Networks (ANNs) have emerged as an important tool for classification problem.
📊
Key Findings & Data:The classification is carried out through data of wheat grains (#200) acquired using image-processing techniques (IPTs).
💡
Conclusion & Impact:These results point out that the ANN trained by ABC optimization algorithm can be successfully applied to classification of wheat grains
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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

🔖

Cite This Article

KAYABAŞI, A. (2018). An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains. International Journal of Intelligent Systems and Applications in Engineering, Cilt: 6 - Sayı: 1, 85–91. https://ajindex.com/articles/173269

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. P. Zapotoczny (2011). Discrimination of wheat grain varieties using image analysis: morphological features. Eur Food Res Technol. 233 769–779
  12. 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
  13. 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. [