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Proceedings Paper

Fingerprint recognition of alien invasive weeds based on the texture character and machine learning
Author(s): Jia-Jia Yu; Xiao-Li Li; Yong He; Zheng-Hao Xu
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Paper Abstract

Multi-spectral imaging technique based on texture analysis and machine learning was proposed to discriminate alien invasive weeds with similar outline but different categories. The objectives of this study were to investigate the feasibility of using Multi-spectral imaging, especially the near-infrared (NIR) channel (800 nm±10 nm) to find the weeds' fingerprints, and validate the performance with specific eigenvalues by co-occurrence matrix. Veronica polita Pries, Veronica persica Poir, longtube ground ivy, Laminum amplexicaule Linn. were selected in this study, which perform different effect in field, and are alien invasive species in China. 307 weed leaves' images were randomly selected for the calibration set, while the remaining 207 samples for the prediction set. All images were pretreated by Wallis filter to adjust the noise by uneven lighting. Gray level co-occurrence matrix was applied to extract the texture character, which shows density, randomness correlation, contrast and homogeneity of texture with different algorithms. Three channels (green channel by 550 nm±10 nm, red channel by 650 nm±10 nm and NIR channel by 800 nm±10 nm) were respectively calculated to get the eigenvalues.Least-squares support vector machines (LS-SVM) was applied to discriminate the categories of weeds by the eigenvalues from co-occurrence matrix. Finally, recognition ratio of 83.35% by NIR channel was obtained, better than the results by green channel (76.67%) and red channel (69.46%). The prediction results of 81.35% indicated that the selected eigenvalues reflected the main characteristics of weeds' fingerprint based on multi-spectral (especially by NIR channel) and LS-SVM model.

Paper Details

Date Published: 10 February 2009
PDF: 8 pages
Proc. SPIE 7126, 28th International Congress on High-Speed Imaging and Photonics, 712615 (10 February 2009); doi: 10.1117/12.822129
Show Author Affiliations
Jia-Jia Yu, Zhejiang Univ. (China)
Xiao-Li Li, Zhejiang Univ. (China)
Yong He, Zhejiang Univ. (China)
Zheng-Hao Xu, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 7126:
28th International Congress on High-Speed Imaging and Photonics
Harald Kleine; Martha Patricia Butron Guillen, Editor(s)

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