Agriculture (Feb 2022)

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

  • Yuan-Kai Tu,
  • Chin-En Kuo,
  • Shih-Lun Fang,
  • Han-Wei Chen,
  • Ming-Kun Chi,
  • Min-Hwi Yao,
  • Bo-Jein Kuo

DOI
https://doi.org/10.3390/agriculture12020259
Journal volume & issue
Vol. 12, no. 2
p. 259

Abstract

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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.

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