Suxing Liu

and 3 more

Understanding three-dimensional (3D) root traits is essential to improve water uptake, increase nitrogen capture, and raise carbon sequestration from the atmosphere. However, quantifying 3D root traits by reconstructing 3D root models for deeper field-grown roots remains a challenge due to the unknown tradeoff between 3D root-model quality and 3D root-trait accuracy. Therefore, we performed two computational experiments. We first compared the 3D model quality generated by five state-of-the-art open-source 3D model reconstruction pipelines on 12 contrasting genotypes of field-grown maize roots. These pipelines included COLMAP, COLMAP+PMVS (Patch-based Multi-view Stereo), VisualSFM, Meshroom, and OpenMVG+MVE (Multi-View Environment). The COLMAP pipeline achieved the best performance regarding 3D model quality versus computational time and image number needed. Thus, in the second test, we compared the accuracy of 3D root-trait measurement generated by the Digital Imaging of Root Traits 3D pipeline (DIRT/3D) using COLMAP-based 3D reconstruction with our current DIRT/3D pipeline that uses a VisualSFM-based 3D reconstruction (Liu et al., 2021) on the same dataset of 12 genotypes, with 5~10 replicates per genotype. The results revealed that, 1) the average number of images needed to build a denser 3D model was reduced from 3000~3600 (DIRT/3D [VisualSFM-based 3D reconstruction]) to 300~600 (DIRT/3D [COLMAP-based 3D reconstruction]); 2) denser 3D models helped improve the accuracy of the 3D root-trait measurement; 3) reducing the number of images can help resolve data storage capacity problems. The updated DIRT/3D (COLMAP-based 3D reconstruction) pipeline enables quicker image collection without compromising the accuracy of 3D root-trait measurements.

Peter Pietrzyk

and 1 more

Branching patterns in plant roots are associated with complex traits such as stress-tolerance, yield, and the ability for carbon sequestration. The capability of the root system to branch allows the plant to search the soil for water and nutrients. For example, a reduction of higher order roots may determine how well a crop plant tolerates drought, whereas the ability to develop more higher order roots determines how well a crop plant tolerates a nutrient deficient soil. Measurements of traits such as rooting depth, root width or specific root length, however, often fail to capture the complex morphological arrangement of the root system. Therefore, a more rigorous analysis of root branching patterns is highly relevant as they are linked to the ability of plants to respond to abiotic stresses, such as drought and nutrient deficiency. Despite the need, it remains a challenge to extract information about branching patterns due to intersecting and overlapping roots in 2D and 3D imaging data. Such occlusion problems add ambiguity and outliers to root trait measurements. We present an algorithm to resolve such intersections in a globally optimal way based on simple heuristics such as straightness of roots - thus being dimension independent. This will enable quantitative analysis of how root branching patterns change in response to abiotic stress using shape descriptors. The possibility to computationally measure very dense branching structures with thousands of intersections will support the breeding of plants that withstand increasing areas of drought and nutrient deficiencies in the world.