Open AccessA Method of Distinguishing Tea varieties Based on Hyperspectral Imaging
Author(s) -
Zhong Yanqi,
Kang Zhiliang,
Peng Wang,
Xiong Luo
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1617/1/012061
Subject(s) - hyperspectral imaging , pattern recognition (psychology) , artificial intelligence , preprocessor , support vector machine , discriminant , mathematics , linear discriminant analysis , computer science
In order to realize the rapid and non-destructive identification of tea varieties, this paper based on hyperspectral imaging technology to find the optimal discrimination model of tea varieties. This article is mainly divided into three aspects: the discriminant model of tea varieties based on spectral characteristics, the discriminant model of tea varieties based on image features, and the discriminant model of tea varieties based on spectral-image fusion features. The experimental results show that Model 1 uses the full-spectrum feature combined with support vector machine (SVM) model, which can distinguish the accuracy of different tea varieties up to 100%. Model 2 is based on the GLCM texture feature based on the characteristic gray image combined with the SVM model, and the discrimination accuracy of different tea varieties reaches 100%. Model 3 discusses the impact of different preprocessing methods on the accuracy of classification under the fusion of two information features, determines Minmax as the best preprocessing method, and obtains 100% classification accuracy in the test set.