Intelligent Classification of Orange Growing Areas by Using Near-Infrared Spectra

Xia Jiang, Yifan Cai, Simon Yang, Gauri Mittal


Near-Infrared spectroscopy (NIR) is a fast and non-destructive method to identify orange growingareas. In this paper, a principal component analysis (PCA) approach was used to obtain the featuresof orange NIR spectra by reducing the divisions in the analysis. An artificial neural network (ANN)was developed to achieve enhanced classification accuracy, while a support vector machine (SVM)model was proposed for higher classification accuracy. A hybrid genetic algorithm (GA) SVMmodel was designed, with the most valuable data from the PCA selecting by GA. The simulationresults showed that the hybrid GA-SVM classifier achieved the best accuracy of 89.717%.

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