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.
Highlights
Crop growth and yield are subject to both biotic and abiotic environmental factors
The spectral data of each measurement were labeled according to corresponding physiological data and used as the input data set to construct the partial least squares discriminant analysis (PLSDA), random forest (RF), 1D-convolutional neural network (CNN), and the proposed 1D-SP-Net
We proposed the 1D-SP-Net embedded with residual blocks and global context (GC) blocks to predict the early physiological drought status of tomato seedlings
Summary
Crop growth and yield are subject to both biotic and abiotic environmental factors. Due to ongoing severe climate change, these factors frequently exceed critical levels and induce various types of plant stress [1]. The implementation of proper management before the occurrence of irreversible damage and yield loss is both efficient and effective [4] Both solutions require deep understanding of the progression of stress induction, for which methodologies capable of detecting stress responses in the early stages are essential [5,6]. Those methodologies include the determination of specific gene expression by polymerase chain reaction technology, quantification of enzyme activity and of active compounds by zymography, spectrometry, and chromatography analysis, and the direct or indirect measurement of other physiological parameters by various technologies and methods [7,8,9,10,11]. The development of nondestructive, rapid, and high-volume remote sensing technique is a promising option for the detection of plant disease and stress [12,13]
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