Identify and Classify Normal and Defects of Prunus_armeniaca Using Imaging Techniques

Abstract

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.

Keywords

• Prunus_armeniaca , GLCM ,SVM, K-mean