Plant phenotyping plays a vital role in plant genetics and breeding programs, providing the foundation for screening and evaluating genetic diversity and linking phenotypic parameters to the genetic determinants of trait expression. This process is critical for identifying molecu
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Plant phenotyping plays a vital role in plant genetics and breeding programs, providing the foundation for screening and evaluating genetic diversity and linking phenotypic parameters to the genetic determinants of trait expression. This process is critical for identifying molecular markers and accelerating genetic breeding improvement, thereby enhancing plant resilience to biotic and abiotic stresses such as drought, salinity, and diseases. Recent advancements in 3D sensing technology have empowered researchers to extract precise phenotypic parameters from plant point clouds, enabling more detailed and accurate plant phenotyping. A critical step in point cloud-based plant phenotyping is plant organ segmentation. Among the available segmentation methods, skeleton-based approaches are simple and intuitive, and could leveraging both local and global geometric information from plant point clouds to facilitate accurate organ segmentation. These methods have gained considerable attention in recent years. However, plant skeletonization, the core component of these approaches, remains limited in handling leafy plants, especially herbaceous species with complex shoot architectures, lateral stems, and multiple leaves. These structural complexities pose challenges that current skeletonization techniques struggle to address effectively.
To address this challenge, we propose a skeleton-based plant organ segmentation framework that accurately extracts curve skeletons from individual plant point clouds and performs precise stem-leaf segmentation based on these skeletons. Our framework is particularly effective in handling leafy plants. It preserves fine structural details during skeletonization while avoiding abnormal or noisy local branches by extending the Laplacian-based contraction (LBC) algorithm through the integration of the Constrained Laplacian Operator. Moreover, we introduce Adaptive Constraints and Tip Points Preservation within the contraction loops to further refine skeleton quality. Additionally, a modified Locally Optimal Projection (LOP) operator is utilized to perform skeleton points calibration, ensuring that the extracted skeleton is centrally aligned with the original plant shape. Furthermore, to evaluate the performance of our proposed framework, we contribute a photogrammetric 3D plant point cloud dataset of 56 Polygonum lapathifolium plants, complete with detailed annotations. Experiment results demonstrate that our framework robustly handles various shapes and sizes of leafy plants and tree branches.
In conclusion, our study enhances the LBC algorithm by integrating the Constrained Laplacian Operator, Adaptive Constraints, and Tip Points Preservation. These improvements increase the accuracy and quality of curve skeleton extraction from leafy plant point clouds, enabling satisfactory plant organ segmentation.
