Classification of corn kernels grades using image analysis and support vector machine

Abstract

In order to classify the quality of corn kernels in an affordable, convenient, and accurate manner, a method based on image analysis and support vector machine is proposed. A total of 129 corn kernels with Grade A, Grade B, and Grade C are used for the experiments. Six typical characteristic parameters of samples are extracted as the characteristic groups. Four different classifiers are applied and compared: support vector machine-genetic algorithm, support vector machine-particle swarm optimization, support vector machine-grid search optimization, and back-propagation neural networks. Experimental results show that the support vector machine and back-propagation neural networks without parameter optimization have the same classification accuracy rates of 92.31%. The classification accuracies are improved using the support vector machine optimization algorithms. The average correct classification rates of support vector machine-genetic algorithm and support vector machine-particle swarm optimization are all 97.44%, while the correct classification rate of support vector machine-grid search achieves 94.87%. It is concluded that the support vector machine algorithm based on parameter optimization is superior to back-propagation neural networks algorithm, and the parameter optimization effects of genetic algorithm and particle swarm optimization are better than grid search method. With a relatively small number of samples, the support vector machine-genetic algorithm and support vector machine-particle swarm optimization algorithms can improve the grading accuracy of corn kernels.