ISBN: 978-981-18-3959-7 DOI: 10.18178/wcse.2022.06.032
Nondestructive Identification of Cyperus Esculentus Based on Machine Learning and Vis-NIR Hyperspectral Information
Abstract—At present, the detection of Cyperus esculentus is limited to manual testing and testing based on physical and chemical properties, which is time-consuming and damaging, so it is of great significance to establish a non-destructive testing model for the classification of Cyperus esculentus on the market. Vis-NIR hyperspectral images were used to identify three kinds of Cyperus esculentus from different producing areas. The Vis-NIR Hyperspectral system (382.19nm~1026.66 nm) was used to collect 600 samples and extract the region of interest (ROI). Taking the ROI of a single seed as a sample, a total of 600 sample data were obtained. Firstly, the Savitzky- Golay (SG) algorithm is used to preprocess the sample data. Then Competitive Adapative Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) are used to reduce the dimensionality of the preprocessed data. Finally, the reduced-dimension features are input into Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Softmax classifier for training. The results show that the reduced-dimensional data can greatly reduce the data redundancy and then reduce the complexity of the model. The experimental results show that the verification accuracy based on SVM, ELM and Softmax model is up to 85.1%, which can effectively achieve rapid non-destructive testing.
Index Terms—cyperus esculentus, Vis-NIR hyperspectral, dimensionality reduction, machine learning
Jingyi Zhao, Tao Sha, Jiahao Wang, Wanlin Gao
Key Laboratory of Agricultural Information Standardization, Ministry of Agriculture and RuralAffairs, China Agricultural University, Beijing 100083, CHINA
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, CHINA
Cite:Jingyi Zhao, Tao Sha, Jiahao Wang, Wanlin Gao, "Nondestructive Identification of Cyperus Esculentus Based on Machine Learning and Vis-NIR Hyperspectral Information , " Proceedings of 2022 the 12th International Workshop on Computer Science and Engineering (WCSE 2022), pp. 228-235, June 24-27, 2022.