Automated classification of plant water status through morpho-kinematic monitoring of plant movement

• New morpho-kinematic features designed for low-cost plant stress phenotyping. • Distinct kinematic signatures for chronic adaptation and acute stress identified. • Central rosette and old leaf dynamics are key indicators of stress status. • Feature engineering captures trend linearity as a marker of stress adaptation. • A cascade model achieves 93% accuracy in four-class stress classification. Plant motion provides valuable indicators of physiological responses to water stress. In this study, we present a structured image-based approach to define and test morpho-kinematic (MK) traits from lettuce plants subjected to varying irrigation regimes under controlled conditions. Four water availability treatments were imposed − Full Control (FC), Stress Control (SC), Mild Stress (SM), and Severe Stress (SS) − varying in timing, frequency, and intensity of irrigation protocols. Using dense optical flow on time-lapse RGB images, we extracted MK features that link leaf age to motion dynamics. These high-dimensional temporal features were compressed into descriptive and trend-based characteristics for classification. Multi-classification problem was divided into nine sub-tasks, for which feature selection and multiple machine-learning models were tested applying Leave-One-Sample-Out cross-validation. The best models were organised into four explainable hierarchical cascades. The presented system captures enough information to successfully distinguish among subtle differences in plants’ response to water availability dynamics (best architecture cascade obtained 0.93 out of fold balanced accuracy). The framework associating leaf age with MK features along with feature engineering allowed explainability – e.g., central rosette’s features were selected almost twice the expected frequency (19 out of 58) in tasks involving the stress-adapted control (SC), while features capturing linear trends in motion were generally selected over twice as often as simple descriptive statistics (44 vs. 19), proving essential for distinguishing most stress conditions. The MK approach proved effective for differentiating water stress levels, positioning it as a powerful tool for digital phenotyping and a solid foundation for developing advanced temporal-aware models.