A parameter selection of support vector machine with genetic algorithm for citrus quality classification

Citrus quality classification is an important and widely studied topic since it has significant role in its market price determination. Due to citrus quality indicators series nonlinearity and no-stationary, the accuracy of conventional mostly used methods including linear discriminant analysis, K-means clustering and neural network has been limited. The use of support vector machine (SVM) has been shown to be an effective technology to solve classification problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. This paper presents a hybrid approach of support vector machine with genetic algorithm (GA) optimization to determine SVM free parameters for developing the accuracy of classification. The approach is applied to classify citrus quality of three gorges reservoir, China. The results indicate that the approach can give a better quality comprehensive evaluation, and has a high potential to become a useful tool in agriculture.