Enhancing morpho-kinematic analysis for plant water stress classification through leaf movements

• Robust morpho-kinematic framework for plant motion water stress phenotyping • Biological sectoring by leaf age improves stability and robustness • Adaptive opinion pooling eliminates error propagation and improves performance • Dry time encodes context for plant stress response stabilizing classification • Non-linear descriptors ameliorate motion dynamics information and model performance Precise irrigation management requires robust classification of plant water stress. We expanded a morpho-kinematic (MK) framework that derives canopy-movement features from RGB time-lapse imaging evaluating how methodological refinements affect robustness and fine discrimination across four irrigation treatments representing distinct stress histories. The study tested both a biological (Agg) versus an isogonal (Unif) sectoring of the canopy image, within an additive scheme where to the baseline (i.e. flattened MK features, A0) were sequentially added non-linear descriptors (A1), irrigation-context variables (i.e. dry time, A2), and their interactions with baseline (A3). The multi-class problem was decomposed in biologically meaningful binary tasks, and the final classification confronted an adaptive – to the performance obtained in the out-of-fold predictions inside the leave-one-sample-out validation framework – linear opinion pooling (ALOP) ensemble, evaluated across its full parameter space, against hierarchical cascades (HCC). In our combined dataset from two sequential Lactuca sativa L. experiments (144 sample-days) ALOP median outperformed HCC in every configuration, while non-linear and contextual enrichments (A1–A2) produced consistent improvements in terms of prediction stability, variability (for ALOP), and balanced accuracy (BA). The highest balanced accuracy (median BA = 0.96) was reached under Unif scheme in A3, yet the Agg configuration in A2 achieved the best compromise between accuracy (median BA = 0.91) and robustness. Concluding, this study identifies methodological pathways that strengthen resilience and transferability of movement-based water-stress classification, establishing a solid foundation for generalizable, low-cost phenotyping.