A Robust Machine Vision Algorithm Development for Quality Parameters Extraction of Circular Biscuits and Cookies Digital Images

Abstract

Biscuits and cookies are one of the major parts of Indian bakery products. The bake level of biscuits and cookies is of significant value to various bakery products as it determines the taste, texture, number of chocolate chips, uniformity in distribution of chocolate chips, and various features related to appearance of products. Six threshold methods (isodata, Otsu, minimum error, moment preserving, Fuzzy, manual method, and k -mean clustering) have been implemented for chocolate chips extraction from captured cookie image. Various other image processing operations such as entropy calculation, area calculation, parameter calculation, baked dough color, solidity, and fraction of top surface area have been implemented for commercial KrackJack biscuits and cookies. Proposed algorithm is able to detect and investigate about various defects such as crack and various spots. A simple and low cost machine vision system with improved version of robust algorithm for quality detection and identification is envisaged. Developed system and robust algorithm have a great application in various biscuit and cookies baking companies. Proposed system is composed of a monochromatic light source, and USB based 10.0 megapixel camera interfaced with ARM-9 processor for image acquisition. MATLAB version 5.2 has been used for development of robust algorithms and testing for various captured frames. Developed methods and procedures were tested on commercial biscuits resulting in the specificity and sensitivity of more than 94% and 82%, respectively. Since developed software package has been tested on commercial biscuits, it can be programmed to inspect other manufactured bakery products.