A 3D Modeling Framework for Quantifying Variation in Soybean Root Structure Architecture

Abstract

Root system architecture (RSA) underpins plant access to water and nutrients, making its characterization critical for improving crop performance in environments with limited soil fertility. However, current methods for quantifying root features face several challenges. They may rely on 2D images that suffer from occlusion, use expensive sensing technologies like X-ray computed tomography, or depend on 3D modeling approaches with assumptions about branching that make them difficult to generalize. To address these challenges, we introduce an open-source Python framework for quantifying RSA samples from 3D point clouds generated from low-cost photogrammetry. Critically, this method incorporates no assumptions about taxon-specific branching orientation, making it both well-suited for modeling naturally grown annual dicots such as soybean and generalizable across species. Using field-grown soybean as a test case, we demonstrate the utility of this framework to extract biologically meaningful 3D features of divergent root systems sampled across developmental stages and soil environments, and enable new analyses not possible with 2D approaches, such as modeling metabolic scaling relationships. Results indicate that, in our soybean samples, while certain individual features like taproot tortuosity are potentially influenced by the soil environment, and while roots in sandy loam exhibited greater feature plasticity, fundamental scaling properties remain consistent. By combining low-cost photogrammetry with 3D reconstruction of root systems from point clouds, this approach provides the plant science community with new opportunities for more comprehensive root studies.