Variety recognition of Chinese cabbage seeds by hyperspectral imaging combined with machine learning

The variety of Chinese cabbage seeds were recognized using hyperspectral imaging with 256 bands from 874 to 1,734 nm in the present paper. A total of 239 Chinese cabbage seed samples including 8 varieties were acquired by hyperspectral image system, 158 for calibration and the rest 81 for validation...

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Veröffentlicht in:Guang pu xue yu guang pu fen xi Jg. 34; H. 9; S. 2519
Hauptverfasser: Cheng, Shu-Xi, Kong, Wen-Wen, Zhang, Chu, Liu, Fei, He, Yong
Format: Journal Article
Sprache:Chinesisch
Veröffentlicht: China 01.09.2014
Schlagworte:
Algorithms
Artificial Intelligence
Brassica
Models, Theoretical
Seeds - classification
Spectroscopy, Near-Infrared
Support Vector Machine
ISSN:1000-0593
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Zusammenfassung:The variety of Chinese cabbage seeds were recognized using hyperspectral imaging with 256 bands from 874 to 1,734 nm in the present paper. A total of 239 Chinese cabbage seed samples including 8 varieties were acquired by hyperspectral image system, 158 for calibration and the rest 81 for validation. A region of 15 pixel x 15 pixel was selected as region of interest (ROI) and the average spectral information of ROI was obtained as sample spectral information. Multiplicative scatter correction was selected as pretreatment method to reduce the noise of spectrum. The performance of four classification algorithms including Ada-boost algorithm, extreme learning machine (ELM), random forest (RF) and support vector machine (SVM) were examined in this study. In order to simplify the input variables, 10 effective wavelengths (EMS) including 1,002, 1,005, 1,015, 1,019, 1,022, 1,103, 1,106, 1,167, 1,237 and 1,409 nm were selected by analysis of variable load distribution in PLS model. The reflectance of effective wavele
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1000-0593
DOI:10.3964/j.issn.1000-0593(2014)09-2519-04