Hyperbolic Few-Shot Learning for Taxonomic Plant Classification

Few-shot classification of biological species remains a challenging problem, especially when taxonomic hierarchies must be respected. In this paper, we investigate the role of hyperbolic geometry in modeling plant taxonomy within the PlantCLEF dataset. We introduce HProtoNet, a hyperbolic prototypical network variant, and compare it against Euclidean Prototypical Networks and Matching Networks. Through hierarchical accuracy analysis, few-shot comparisons, and Poincar\'e disk embedding visualizations, we demonstrate that hyperbolic embeddings better capture the inherent tree-like structure of species relationships. Our results highlight the promise of geometry-aware few-shot learning for biodiversity applications. We further argue that these methods not only improve classification but also align computational predictions with biological intuition, making them particularly suitable for ecological deployment.