PlantPose: Universal Plant Skeleton Estimation via Tree-constrained Graph Generation

TL;DR

PlantPose is a novel universal method for estimating plant skeletons from images by combining graph generation with traditional algorithms, capable of handling diverse plant types and topologies.

Contribution

Introduces PlantPose, a new approach that enforces tree constraints during graph generation, with a large dataset for improved generalization across plant species.

Findings

Robust and accurate skeleton estimation across multiple domains.

Effective generalization to unseen plant categories and styles.

Demonstrates strengths and limitations in complex data scenarios.

Abstract

Accurate estimation of plant skeletal structures (e.g., branching structures) from images is essential for smart agriculture and plant science. Unlike human skeletons with fixed topology, plant skeleton estimation presents a unique challenge, i.e., estimating arbitrary tree graphs from images. To address this problem, we introduce PlantPose, a universal plant skeleton estimator via tree-constrained graph generation. PlantPose combines learning-based graph generation with traditional graph algorithms to enforce tree constraints during the training loop. To enhance the model's generalization capability, we curate a large and diverse dataset comprising real-world and synthetic plant images, along with simplified representations (e.g., sketches and abstract drawings). This dataset enables the generalized model to adapt to diverse input styles and categories of plant images while preserving topological consistency. Our approach demonstrates robust and accurate plant skeleton estimation across multiple domains, including previously unseen out-of-domain scenarios. Further analyses highlight the method's strengths and limitations in handling complex, heterogeneous data distributions. All implementations and datasets are available at https://github.com/huntorochi/PlantPose/.