Application of Blind Sources Separation in plant leaves classification

This paper discussed the application of Blind Sources Separation (BSS) in plant leaves classification. Firstly, collection of two different types of plant leaves was performed using the Nexus-870 Fourier transform infrared spectroscopy, and wavelet analysis was adopted to compress the immense sample...

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Vydané v:2012 10th World Congress on Intelligent Control and Automation (WCICA s. 4174 - 4179
Hlavní autori: Wu Ying, Guo Tian-tai, Jiang Jie-wei
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.07.2012
Predmet:
Accuracy
Algorithm design and analysis
Blind sources separation (BSS)
BP neural network
Classification algorithms
near infrared (NIR) spectroscopy
Neural networks
Plant leaves
Spectroscopy
Training
wavelet analysis
Wavelet transforms
ISBN:9781467313971, 1467313971
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Abstract This paper discussed the application of Blind Sources Separation (BSS) in plant leaves classification. Firstly, collection of two different types of plant leaves was performed using the Nexus-870 Fourier transform infrared spectroscopy, and wavelet analysis was adopted to compress the immense sample data, thus accelerating the data processing speed. Then the BSS algorithm FastICA algorithm was used on the compressed spectral data to increase the difference between the different signals. Finally, BP neural network algorithm was used to achieve the classification of plant species. Experiments showed that processing data in near-infrared spectroscopy through BSS can not only improve the speed and accuracy of BP neural network, but also enhance its classification correctness, and the classification results with the proposed method was satisfactory.
AbstractList This paper discussed the application of Blind Sources Separation (BSS) in plant leaves classification. Firstly, collection of two different types of plant leaves was performed using the Nexus-870 Fourier transform infrared spectroscopy, and wavelet analysis was adopted to compress the immense sample data, thus accelerating the data processing speed. Then the BSS algorithm FastICA algorithm was used on the compressed spectral data to increase the difference between the different signals. Finally, BP neural network algorithm was used to achieve the classification of plant species. Experiments showed that processing data in near-infrared spectroscopy through BSS can not only improve the speed and accuracy of BP neural network, but also enhance its classification correctness, and the classification results with the proposed method was satisfactory.
Author Wu Ying
Guo Tian-tai
Jiang Jie-wei
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  organization: China Jiliang Univ., Hangzhou, China
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  surname: Jiang Jie-wei
  fullname: Jiang Jie-wei
  organization: China Jiliang Univ., Hangzhou, China
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Snippet This paper discussed the application of Blind Sources Separation (BSS) in plant leaves classification. Firstly, collection of two different types of plant...
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StartPage 4174
SubjectTerms Accuracy
Algorithm design and analysis
Blind sources separation (BSS)
BP neural network
Classification algorithms
near infrared (NIR) spectroscopy
Neural networks
Plant leaves
Spectroscopy
Training
wavelet analysis
Wavelet transforms
Title Application of Blind Sources Separation in plant leaves classification
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