Fingerprint recognition of alien invasive weeds based on the texture character and machine learning

Jia-Jia Yu; Xiao-Li Li; Yong He; Zheng-Hao Xu

doi:10.1117/12.822129

10 February 2009 Fingerprint recognition of alien invasive weeds based on the texture character and machine learning

Jia-Jia Yu, Xiao-Li Li, Yong He, Zheng-Hao Xu

Proceedings Volume 7126, 28th International Congress on High-Speed Imaging and Photonics; 712615 (2009) https://doi.org/10.1117/12.822129
Event: 28th International Congress on High-Speed Imaging and Photonics, 2008, Canberra, Australia

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.

Citation Download Citation

Jia-Jia Yu, Xiao-Li Li, Yong He, and Zheng-Hao Xu "Fingerprint recognition of alien invasive weeds based on the texture character and machine learning", Proc. SPIE 7126, 28th International Congress on High-Speed Imaging and Photonics, 712615 (10 February 2009); https://doi.org/10.1117/12.822129

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KEYWORDS

Near infrared

Image processing

Machine learning

Calibration

Feature extraction

Fingerprint recognition

CCD cameras

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