Image-based Morphological Characterization of Filamentous Biological Structures with Non-constant Curvature Shape Feature

Tendrils coil their shape to anchor the plant to supporting structures, allowing vertical growth toward light. Although climbingplants have been studied for a long time, extracting information regarding the relationship between the temporal shape change,the event that triggers it, and the contact location is still challenging. To help build this relation, we propose an image-basedmethod by which it is possible to analyze shape changes over time in tendrils when mechano-stimulated in different portionsof their body. We employ a geometric approach using a 3D Piece-Wise Clothoid-based model to reconstruct the configurationtaken by a tendril after mechanical rubbing. The reconstruction shows high robustness and reliability with an accuracyof R2> 0.99. This method demonstrates distinct advantages over deep learning-based approaches, including reduced datarequirements, lower computational costs, and interpretability. Our analysis reveals higher responsiveness in the apical segmentof tendrils, which might correspond to higher sensitivity and tissue flexibility in that region of the organs. Our study providesa methodology for gaining new insights into plant biomechanics and offers a foundation for designing and developing novelintelligent robotic systems inspired by climbing plants.