Identification of Defective Mangoes using Gabor Wavelets: A Non-Destructive Technique Based on Texture Analysis

In many circumstances, texture is the only information that can be used in natural image analysis because it is an important property of the surface that characterizes it’s nature. Texture is defined as a spatial arrangement of local (gray level) intensity attributes which are correlated within areas of visual scene corresponding to surface regions. An image region has a constant texture if sets of its local properties in that region are constant. Thus texture analysis has received considerable attention in the field of image analysis and pattern recognition. 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. In the literature, the researchers worldwide have been working in various texture analysis algorithms for different applications like detection, recognition, classification, segmentation, clustering etc. Many algorithms suffer from low sensitive detection, difficult back ground adaption and high memory requirement. Problems and limitations associated with the available techniques have been reported by many studies. Each has some drawback under all lighting conditions and no one has used a robust, reliable algorithm for detection of spongy tissue in alphonso mango under real life test environment. To develop an optimized algorithm using a non contact mechanism which will detect the defective alphonso mangoes happen to be a challenging task. The objective of the proposed research work is to develop a database using non-contact imaging technique like digital X-ray and to obtain computationally cost effective and noncontact solution that achieve better recognition rate under various conditions in consultation with the agriculture scientist. In this paper we have proposed use of Gabor wavelets for extraction of textural features that identifies Spongy Tissue in alphonso mango successfully. Performance of the proposed algorithm is carried out on the generated database [1] that is easily available for the researchers working on the said area. Experimental results computed using the proposed algorithm and manual validation process with the cut sections of the alphonso mangoes show that identification rate of defective mangoes for the said database was found to be 93.6%.