Effects of Feature Selection Using Binary Particle Swarm Optimization on Wheat Variety Classification

In this article, classification of wheat varieties is aimed with the help of multiclass support vector machines (M-SVM) and binary particle swarm optimization (BPSO) algorithm. For each wheat kernel, 9 geometric and 3 color features are obtained from the digital images which are belong to 5 wheat type. Wheat types are classified using M-SVM. In order to increase the reliability of the classification process, 2 fold cross validation approach is implemented and this process repeated 250 times. Average classification accuracy is obtained as 91.5%. With the aim of increasing the classification accuracy and decreasing the process time, descriptive features are decreased by BPSO algorithm and reduced from 12 to 7. Average of classification accuracy is obtained as 92.02% using 7 features obtained from reduction with BPSO.