Defect Detection in Alphonso using Statistical Method and Principal Component Analysis: A Non-Destructive Approach

Natural image analysis uses textural property of the surface. Texture is defined as a spatial arrangement of local intensity attributes that are correlated within areas of visual scene corresponding to surface regions. Texture exhibits some sort of periodicity of the basic pattern of Spongy Tissue in alphonso mango. This leads to use textural property to identify different patterns of Spongy Tissue in alphonso for detection of defects in alphonso mango. Visual assessment of texture made by human is time consuming and inspection made by human does not achieve a high degree of accuracy and preciseness. Automated visual inspection of the textural pattern improves the accuracy and preciseness during detection of defects in alphonso mango. To develop an optimized algorithm using a non contact mechanism which will detect the defective alphonso mangoes happen to be a challenging task. In this paper we have proposed use of subspace analysis technique for extraction of textural features that identifies Spongy Tissue in alphonso mango successfully. This paper presents a methodology that combines the principal component analysis (PCA) to locate the defect in alphonso mango effectively with moment of image segment as statistical feature and Fuzzy C-Means as a data clustering technique used for classification. The proposed algorithm performance is checked on the generated database [1] that is easily available for the researchers working on the said area. Experimental results computed using the proposed algorithm has been validated manually with the cut sections of the alphonso mangoes.