Detection of Foreign Materials in Wheat Kernels Using Regional Colour Descriptors
Present paper reports on the development of an automated machine vision system for detection of foreign materials in wheat kernels using regional color descriptors. The said pipeline was executed in the form of an integrated flowing pipeline after having proper choice of different possible alternatives for different stages of image processing. A new type of surface colour descriptor is also proposed in this work to define wheat kernel uniquely. The fifteen-element colour descriptor is executed after having thorough comparison of six different colour spaces, each having 72 separate quantifiable components. The fifteen elements of the proposed colour-descriptor, extracted from each segmented region of the sample image, are concatenated in the form of an input to the neural classifier. The neural classifier is trained with Levenberg-Marquardt (LM) learning algorithm to achieve an extremely fast convergence. The recognition rate of the executed classifier is found to be more than 99.2% for detection of impurity in unconnected wheat kernels. The results of present investigations are quite promising. The proposed system has potential future in the field of machine vision based quality inspection of wheat and other cereal grains.