Demeter: A Parametric Model of Crop Plant Morphology from the Real World

2025-10-18

AI Summary

Existing parametric models excel in human and animal modeling but struggle to capture the topological diversity and non-rigid deformations inherent in plant morphology. This paper introduces the first cross-species 3D parametric model for crops that supports variable topology, unifying three types of morphological variation: joint articulation, sub-component deformation, and global non-rigid deformation. Our method leverages a large-scale, field-collected soybean dataset and employs data-driven deep learning to construct a compact, interpretable parametric shape representation. Experiments demonstrate state-of-the-art performance across shape synthesis, structural reconstruction, and biophysical simulation tasks. The model enables accurate, topology-aware geometric reasoning for complex plant structures under growth and environmental perturbations. To foster reproducibility and downstream applications, we publicly release both code and dataset. This work establishes a foundational tool for agricultural computer vision, digital twin construction, and intelligent crop breeding.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Learning 3D parametric shape models of objects has gained popularity in vision and graphics and has showed broad utility in 3D reconstruction, generation, understanding, and simulation. While powerful models exist for humans and animals, equally expressive approaches for modeling plants are lacking. In this work, we present Demeter, a data-driven parametric model that encodes key factors of a plant morphology, including topology, shape, articulation, and deformation into a compact learned representation. Unlike previous parametric models, Demeter handles varying shape topology across various species and models three sources of shape variation: articulation, subcomponent shape variation, and non-rigid deformation. To advance crop plant modeling, we collected a large-scale, ground-truthed dataset from a soybean farm as a testbed. Experiments show that Demeter effectively synthesizes shapes, reconstructs structures, and simulates biophysical processes. Code and data is available at https://tianhang-cheng.github.io/Demeter/.

Problem

• Modeling plant morphology with varying topology across species
• Encoding articulation, shape variation and deformation in plants
• Creating parametric models for crop plant simulation and reconstruction

Innovation

• Learned parametric model encodes plant morphology factors
• Handles varying topology across species and shape variations
• Uses large-scale dataset for synthesis and simulation tasks