Identification of Peanut Pods with Three or More Kernels by Machine Vision and Neural Network

Abstract Separation of unshelled peanuts containing three or more kernels and then niche marketing them can potentially increase the value of unshelled peanuts and thus the profit of peanut producers or processors. Effective identification of peanut pods with three or more kernels is a critical step prior to separation. In this study, a machine vision system was teamed up with neural network technique to discriminate unshelled peanuts into two groups: one with three or more kernels and the other with two or less kernels. A set of physical features including the number of bumps, projected area, length and perimeter, etc., were extracted from the images taken and used to train an artificial neural network for discriminating the peanuts. It was found that among all the selected features, the length, the major axis length and perimeter have the best correlation with the number of kernels (correlation coefficient r = 0.87–0.88); the area and convex area have good correlation ( r = 0.85); the eccentricity, number of bumps, and the compactness have relatively lower correction ( r = 0.77–0.80); the solidity and the minor axis length have the least correlation to the number of kernels ( r = −0.415–0.26). The best discrimination accuracy obtained for peanut pods with three or more kernels was 92.5% for the conditions used in this study.