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Monitoring of Paddy Rice Varieties Based on the Combination of the Laser-Induced Fluorescence and Multivariate Analysis

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Abstract

Paddy rice is one of three major cereal crops in China, and the number of the paddy rice variety is increasing rapidly. The paddy rice variety is strongly related to crop yield and is also difficult to classify by using the naked eyes. A reliable approach is essential for accurately identifying different paddy rice varieties. Laser-induced fluorescence (LIF) technology has been widely utilized in many fields due to its particular advantages (rapid, non-intrusive, and sensitive). Thus, LIF combined with multivariate analysis that contained principal component analysis (PCA) and support vector machine (SVM) was proposed and was attempted to be utilized to identify different paddy rice varieties in this investigation. These fluorescence spectra displayed a high degree of multi-collinearity, and about 96.58% of the total variance contained in the laser-induced fluorescence spectra which were excited by a 532-nm excited wavelength can be explained by using the first three principle components. A SVM model with the help of threefold cross validation was used for paddy rice variety identification based on new variables calculated utilizing PCA. The numerical and experimental results displayed by using a confusion matrix and the classification accuracy can reach up to 91.36%. Thus, LIF technology combined with multivariate analysis can provide researchers with a faster and more effective tool for identifying different paddy rice varieties.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 41601360), Fundamental Research Funds for the Central Universities (Grant No. 2042016kf0008), Natural Science Foundation of Hubei Province (Grant No. 2015CFA002), and Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (Grant No.15R01). The authors wish to thank the College of Plant Science & Technology of Huazhong Agricultural University for providing the experimental samples.

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Authors and Affiliations

  1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, 430079, China

    Jian Yang, Jia Sun, Lin Du, Biwu Chen, Zhenbing Zhang, Shuo Shi & Wei Gong

  2. School of Physics and Technology, Wuhan University, Wuhan, Hubei, 430072, China

    Lin Du

  3. Collaborative Innovation Center of Geospatial Technology, Wuhan, Hubei, 430079, China

    Shuo Shi & Wei Gong

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  1. Jian Yang
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  2. Jia Sun
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  3. Lin Du
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  4. Biwu Chen
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  5. Zhenbing Zhang
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  6. Shuo Shi
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  7. Wei Gong
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Correspondence to Jian Yang or Wei Gong.

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Jian Yang declares that he has no conflict of interest. Jia Sun declares that she has no conflict of interest. Lin Du declares that he has no conflict of interest. Biwu Chen declares that he has no conflict of interest. Zhenbing Zhang declares that he has no conflict of interest. Shuo Shi declares that he has no conflict of interest. Wei Gong declares that he has no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Yang, J., Sun, J., Du, L. et al. Monitoring of Paddy Rice Varieties Based on the Combination of the Laser-Induced Fluorescence and Multivariate Analysis. Food Anal. Methods 10, 2398–2403 (2017). https://doi.org/10.1007/s12161-017-0809-2

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  • DOI: https://doi.org/10.1007/s12161-017-0809-2

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