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• W2767862511 abstract "Damage of tea plant causes a lot of loss in tea production, but there is not an appropriate method to detect tea plants with pest damage. In this work, electronic nose (E-nose) and Gas Chromatography-Mass Spectrometer (GC-MS), as an auxiliary technique, were employed to detect tea plants with pest damage in two aspects, including tea plants with different invasive severities and with different invasive times, for giving a comprehensive results. A new feature extraction method based on a piecewise function was proposed and its performance was compared with those of the other three commonly employed models-polynomial functions, exponential functions, and Gaussian functions. Feature selection based on principal component analysis (PCA) and multi-layered perceptron (MLP) were employed for further feature reduction and classification, respectively. The results showed that feature extraction based on piecewise function was the best. The combination of feature extraction based on piecewise function, feature selection based on PCA and MLP was the best method and good enough for the classification in tea plants damage area. The results proved that E-nose was able to detect tea plants either with different invasive severities or different invasive times." @default.
• W2767862511 created "2017-11-17" @default.
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• W2767862511 date "2017-12-01" @default.
• W2767862511 modified "2023-10-01" @default.
• W2767862511 title "Discrimination among tea plants either with different invasive severities or different invasive times using MOS electronic nose combined with a new feature extraction method" @default.
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• W2767862511 doi "https://doi.org/10.1016/j.compag.2017.11.007" @default.
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