This paper presents a novel Graph Optimal Transport (Graph OT) framework for analyzing and aligning plant structures across different growth stages and transformations. Our method extends existing graph matching techniques by incorporating domain-specific botanical features and employing a multi-scale matching strategy that captures both local and global structural characteristics. The framework combines multiple feature representations, including node descriptors, spectral embeddings, Node2Vec embeddings, and relative positions, to construct an augmented cost matrix for optimal transport based matching. We evaluated our approach on a dataset of 50 distinct plant structures under various transformations, including rotation, deformation, and partial matching scenarios.

The results indicate that our Graph OT framework significantly outperforms traditional optimal transport (OT) methods, achieving node-matching accuracy scores of 0.75 for rotated,
0.74 for deformed structures, 0.67 for cut structures, and 0.71 for structures with skipped nodes. Our approach demonstrates particular robustness in handling complex transformations. This method provides a powerful tool for botany applications such as crop management, growth modeling, and automated pruning systems.