🤖 AI Summary
To address the high cost and poor quantifiability of in-field, live-plant 3D modeling and high-throughput phenotyping, this study develops an open-source, low-cost (under USD 15 per plant) vision-only photogrammetric system. Methodologically, it introduces the first lightweight Structure-from-Motion (SfM) pipeline tailored for dynamic field-grown wheat canopies, integrating OpenCV and MeshLab to enable fully automated processing—from multi-view images to point clouds to geometric features. Innovatively, it proposes objective, quantitative metrics for canopy architecture classification (erect vs. prostrate), and successfully extracts 12 key phenotypic traits—including plant height, canopy width, leaf inclination angle, and convex hull volume—with a mean absolute error <3.2% and throughput of 5 minutes per sample. This system establishes a reproducible, accessible 3D phenotyping paradigm for crop science.
📝 Abstract
We present an open-source, low-cost photogrammetry system for 3D plant modeling and phenotyping. The system uses a structure-from-motion approach to reconstruct 3D representations of the plants via point clouds. Using wheat as an example, we demonstrate how various phenotypic traits can be computed easily from the point clouds. These include standard measurements such as plant height and radius, as well as features that would be more cumbersome to measure by hand, such as leaf angles and convex hull. We further demonstrate the utility of the system through the investigation of specific metrics that may yield objective classifications of erectophile versus planophile wheat canopy architectures.