Image-based Morphological Characterization of Filamentous Biological Structures with Non-constant Curvature Shape Feature
This work addresses the challenge of understanding plant biomechanics for researchers, offering a method that could inspire robotic design, though it is incremental as it builds on existing geometric modeling techniques.
The authors tackled the problem of analyzing shape changes in tendrils after mechanical stimulation by proposing an image-based method using a 3D Piece-Wise Clothoid-based model, achieving high accuracy with R2 > 0.99 and demonstrating advantages over deep learning approaches in data efficiency and interpretability.
Tendrils coil their shape to anchor the plant to supporting structures, allowing vertical growth toward light. Although climbing plants 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-based method by which it is possible to analyze shape changes over time in tendrils when mechano-stimulated in different portions of their body. We employ a geometric approach using a 3D Piece-Wise Clothoid-based model to reconstruct the configuration taken by a tendril after mechanical rubbing. The reconstruction shows high robustness and reliability with an accuracy of R2 > 0.99. This method demonstrates distinct advantages over deep learning-based approaches, including reduced data requirements, lower computational costs, and interpretability. Our analysis reveals higher responsiveness in the apical segment of tendrils, which might correspond to higher sensitivity and tissue flexibility in that region of the organs. Our study provides a methodology for gaining new insights into plant biomechanics and offers a foundation for designing and developing novel intelligent robotic systems inspired by climbing plants.