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A 1D-SP-Net to Determine Early Drought Stress Status of Tomato ( Solanum lycopersicum ) with Imbalanced Vis/NIR Spectroscopy Data

Author

Listed:
  • Yuan-Kai Tu

    (Division of Biotechnology, Taiwan Agricultural Research Institute, Taichung 41362, Taiwan
    These authors contributed equally to this work.)

  • Chin-En Kuo

    (Department of Applied Mathematics, National Chung Hsing University, Taichung 40227, Taiwan
    These authors contributed equally to this work.)

  • Shih-Lun Fang

    (Department of Agronomy, College of Agriculture and Nature Resources, National Chung Hsing University, Taichung 40227, Taiwan)

  • Han-Wei Chen

    (Division of Biotechnology, Taiwan Agricultural Research Institute, Taichung 41362, Taiwan)

  • Ming-Kun Chi

    (Division of Biotechnology, Taiwan Agricultural Research Institute, Taichung 41362, Taiwan)

  • Min-Hwi Yao

    (Division of Agricultural Engineering, Taiwan Agricultural Research Institute, Taichung 41362, Taiwan)

  • Bo-Jein Kuo

    (Department of Agronomy, College of Agriculture and Nature Resources, National Chung Hsing University, Taichung 40227, Taiwan)

Abstract

Detection of the early stages of stress is crucial in stabilizing crop yields and agricultural production. The aim of this study was to construct a nondestructive and robust method to predict the early physiological drought status of the tomato ( Solanum lycopersicum ); for this purpose, a convolutional neural network (CNN)-based model with a one-dimensional (1D) kernel for fitting the visible and near infrared (Vis/NIR) spectral data was proposed. To prevent degradation and enhance the feature comprehension of the deep neural network architecture, residual and global context modules were embedded in the proposed 1D-CNN model, yielding the 1D spectrogram power net (1D-SP-Net). The 1D-SP-Net outperformed the 1D-CNN, partial least squares discriminant analysis (PLSDA), and random forest (RF) models in model testing, demonstrating an accuracy of 96.3%, precision of 98.0%, Matthew’s correlation coefficient of 0.92, and an F1 score of 0.95. Furthermore, when employing various synthesized imbalanced data sets, the proposed 1D-SP-Net remained robust and consistent, outperforming the other models in terms of the prediction capabilities. These results indicate that the 1D-SP-Net is a promising model resistant to the effects of imbalanced data sets and able to determine the early drought stress status of tomato seedlings in a non-invasive manner.

Suggested Citation

  • Yuan-Kai Tu & Chin-En Kuo & Shih-Lun Fang & Han-Wei Chen & Ming-Kun Chi & Min-Hwi Yao & Bo-Jein Kuo, 2022. "A 1D-SP-Net to Determine Early Drought Stress Status of Tomato ( Solanum lycopersicum ) with Imbalanced Vis/NIR Spectroscopy Data," Agriculture, MDPI, vol. 12(2), pages 1-17, February.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:259-:d:747084
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    References listed on IDEAS

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