Abstract
Growth year is an important factors that influence the quality and price of ginseng. In this paper, based on the terahertz spectrum, we propose a method for classification of Ginseng with different growth ages by support vector machine (SVM) model based on an improved cuckoo search (CS) algorithm named SPCS. The CS algorithm is improved from step size and discovery probability to balance the global search capability and the local convergence, and make the SVM model get better optimization ability for the penalty factor and kernel function parameter. Compared with SVM based on genetic algorithm (GA-SVM) and SVM based on CS (CS-SVM), SVM based on SPCS (SPCS-SVM) has better classification ability, the accuracy rate is more than 99.6%. The experimental results show that terahertz spectroscopy coupled with machine learning algorithm is an effective method for classification of Ginseng with different growth ages.
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