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Machine Learning Identification of Saline-Alkali-Tolerant Japonica Rice Varieties Based on Raman Spectroscopy and Python Visual Analysis

Author

Listed:
  • Rui Liu

    (College of Agricultural Engineering, Heilongjiang Bayi Agricultural University, Daqing 163000, China)

  • Feng Tan

    (College of Electrical and Information, Heilongjiang Bayi Agricultural University, Daqing 163000, China)

  • Yaxuan Wang

    (College of Civil Engineering and Water Conservancy, Heilongjiang Bayi Agricultural University, Daqing 163000, China)

  • Bo Ma

    (Qiqihar Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar 161006, China)

  • Ming Yuan

    (Qiqihar Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar 161006, China)

  • Lianxia Wang

    (Qiqihar Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar 161006, China)

  • Xin Zhao

    (College of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, China)

Abstract

The core of saline-alkali land improvement is planting suitable plants. Planting rice in saline-alkali land can not only effectively improve saline-alkali soil, but also increase grain yield. However, traditional identification methods for saline-alkali-tolerant rice varieties require tedious and time-consuming field investigations based on growth indicators by rice breeders. In this study, the visualization method of Python data processing was used to analyze the Raman spectroscopy of japonica rice in order to study a simple and efficient identification method of saline-alkali-tolerant japonica rice varieties. Three saline-alkali-tolerant japonica varieties and three saline-alkali-sensitive japonica varieties were collected from control and saline-alkali-treated fields, respectively, and the Raman spectra of 432 samples were obtained. The data preprocessing stage used filtering-difference method to process Raman spectral data to complete interference reduction and crests extraction. In the feature selection stage, scipy.signal.find_peaks (SSFP), SelectKBest (SKB) and recursive feature elimination (RFE) were used for machine feature selection of spectral data. According to the feature dimension obtained by machine feature selection, dataset partitioning by K-fold CV, the typical linear logistic regression (LR) and typical nonlinear support vector machine (SVM) models were established for classification. Experimental results showed that the typical nonlinear SVM identification model based on both RFE machine feature selection and six-fold CV dataset partitioning had the best identification rate, which was 94%. Therefore, the SVM classification model proposed in this study could provide help in the intelligent identification of saline-alkali-tolerant japonica rice varieties.

Suggested Citation

  • Rui Liu & Feng Tan & Yaxuan Wang & Bo Ma & Ming Yuan & Lianxia Wang & Xin Zhao, 2022. "Machine Learning Identification of Saline-Alkali-Tolerant Japonica Rice Varieties Based on Raman Spectroscopy and Python Visual Analysis," Agriculture, MDPI, vol. 12(7), pages 1-14, July.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:1048-:d:865730
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    References listed on IDEAS

    as
    1. Liping Zhu, 2020. "Review of sparse sufficient dimension reduction: comment," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 4(2), pages 134-134, July.
    2. Giovanni Nattino & Michael L. Pennell & Stanley Lemeshow, 2020. "Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer‐Lemeshow test," Biometrics, The International Biometric Society, vol. 76(2), pages 549-560, June.
    3. Giovanni Nattino & Michael L. Pennell & Stanley Lemeshow, 2020. "Rejoinder to “Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer‐Lemeshow test”," Biometrics, The International Biometric Society, vol. 76(2), pages 575-577, June.
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