Detection and analysis of wheat storage year based on electronic tongue and DWT-IPSO-LSSVM algorithm

Abstract: The electronic tongue system based on virtual instrument technology was used to qualitatively analyze the wheat, which achieves rapid and objective evaluation analysis of aged wheat with different storage years. In view of the complex output signal of the electronic tongue and the large amount of data, the Discrete Wavelet Transform was used to extract the eigenvalues of the original data to reduce the data dimension size. On this basis, the improved particle swarm optimization algorithm was used to optimize the parameters of Least squares support vector machine, and the analysis model of wheat storage age was established. The experiments exhibited that the DWT-IPSO-LSSVM model had better classification performances than other pattern recognition models, such as DWT-GA-LSSVM, DWT-AF-LSSVM and DWT-PSO-LSSVM. The results showed that the accuracy of the training set, the accuracy of the test set, overall classification accuracy and Kappa coefficient of the proposed combined model in this paper were 95%, 92%, 91% and 0.88 respectively. This research indicated that the electronic tongue system combined with proposed model can be used to identify and discriminate the aged wheat with different storage years. Keywords: electronic tongue, wheat storage year, discrete wavelet transform, improved particle swarm optimization, least squares support vector machine DOI:  10.33440/j.ijpaa.20190202.46.  Citation: Guo T T, Yang Z W, Miao N, Zhang X, Li Q S, Guo J, Wang Z Q. Detection and analysis of wheat storage year based on electronic tongue and DWT-IPSO-LSSVM algorithm.  Int J Precis Agric Aviat, 2019; 2(2): 19–24.