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This research demonstrates non-destructive detection of dynamic data in rice plant stress, suggesting effective monitoring through partial least squares discriminant analysis.
in tomatoes and rice both had specific dynamic change rules, and there was a complex cross-regulation mechanism between them. By combining sensor data with partial least squares discriminant analysis (PLS-DA), the classification accuracy of stress types exceeds 95%. This non-destructive and highly sensitive detection system can provide real-time dynamic data of stress signals, bringing a breakthrough to the in-situ monitoring of plant physiological states.
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Chen et al. (2026) studied this question.
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