Identify and Classify Normal and Defects of Prunus_armeniaca Using Imaging Techniques

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Amel H. Abbas Marwa A. Shamel


The Prunus_armeniaca fruit is classified manually in wholesale markets, supermarkets and food processing plants on a normal or defects basis. The aim of this research is to replace the manual sorting techniques using computer vision techniques and applications by proposing techniques for identify and recognitions patterns through the use of 150 fruits of Prunus_armeniaca, 10 for the testing stage in fresh and 10 for testing stage in case of defects. The fruits Prunus_armeniaca collected from growing trees in the large fields of Salah al-Din province\Iraq. The system designed for classification based on the color image taken inside a black box used camera pixel resolution of (13 mega) with a constant intensity of light. . Used K-mean in phase segmentations and only computed 13 features derive statistics from GLCM .classification phase used SVM classify fruit into two class, either (normal or defects) .Results the system success rate reach 100%.The work done using MATLAB R2016a.


Prunus_armeniaca , GLCM ,SVM, K-mean


[1] Apricot , http://www10 Impressive Apricot Benefits | Organic Facts , (2017).
[2] H.Tariq and M.Aqil Burney, “K-Means Cluster Analysis for Image Segmentation”, International Journal of Computer Application, vol. 4,no.4,2014.
[3] A.Khaled , R.Sanjay , and S.Vineet ,” An Efficient K-Means Clustering Algorithm”,1997.
[4] T. Kanungo , M. David Mount,S. Nathan Netanyahu,D. Christine Piatko,S. Ruth ,Y.Angela Wu, “An Efficient k-Means Clustering Algorithm: Analysis and Implementation”, IEEE Transaction on Pattern Analysis and Machine Intelligence,vol.24, pp 881-892 ,2002.
[5] V.Deepali,T. Shweta,M.Geetika, “ Normalization based K means Clustering Algorithm”,International Journal of Advanced Engineering Research and Science (IJAERS), vol.2,2015.
[6] T.Suman ,M. Avi, “Image Segmentation using k-means clustering, EM and Normalized Cuts’’, University Of California Irvine ,2008.
[7] L.Jinhua,S. Shiji,Z. Yuli, and Z. Zhen, “ Robust K-Median and K-Means Clustering Algorithms for Incomplete Data”, Mathematical Problems in Engineering ,2016.
[8] M.Umamaheswari, P. Isakki , “ Myocardial Infarction Prediction Using KMeans Clustering Algorithm ” ,International Journal of Innovative Research in Computer and Communication Engineering , vol. 5, 2017.
[9] L.Martin , “ A Simple Introduction to Support Vector Machines”,CSE 802,2011.
[10] M.David, ” Support Vector Machines: The Interface to libsvm in Package e1071”, 2017.
[11] Z.Yudong and W. Lenan, “Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine”, sensors,vol.12,2012.
[12] D.Matthew Sacchet, P.Gautam, C.Lara Foland-Ross,M. Paul Thompsonm, And H. Ian Gotlib, “Support Vector Machine Classification Of Major Depressive Disorder Using Diffusion-Weighted Neuroimaging And Graph Theory”, Free PMC Article , vol. 6,2015.
[13] S.elvarajah and S.Kodituwakku, “ Analysis and Comparison of Texture Features for Content Based Image Retrieval”, International Journal of Latest Trends in Computing , vol. 2,2012.
[14] G.Shantala, P. Jagadeesh , S. Shivanand , “ Role of GLCM Features in Identifying Abnormalities in the Retinal Images”,MECS,vol.6, pp 45-51,2015.
[15] S.Kanchan, K. Aditi,S. Kulbeer , “ GLCM and its Features”,International Journal of Advanced Research in Electronics and Communication Engineering, vol. 4, 2015.
[16] M.Musa Mokij and H.Pui Ngian, “Vegetable Recognition Based Texture Features’’,Online,2011.
[17] A.Goshtasby, “ Advances in Computer Vision and Pattern Recognition’’, Springer London Dordrecht Heidelberg New York, 2012.
[18] N. Aggarwal, K. Agrawal, “First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images’’, Journal of Signal and Information Processing, Published Online(,pp, vol.3,2012.
[19] W.Nicholas, “ An Investigation into Texture Features for Image Retrieval’’, University of Bath ,2007.
[20] A.Baratloo, M.Hosseini,A. Negida,G. Ashal, “ Simple Definition And Calculation Of Accuracy Sensitivity And Specificity’’, Emergency , vol. 3, 2015.