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.

We quantify the shape of hooked hairs which is a newly observed phenotype of epidermal cell extensions [1] in the common bean genotype L88-57 (Phaseolus vulgaris). The hooked hairs emerge below-ground before the root hairs and have a distinct ‘hooking’ morphology. We generated a dataset capturing their full distribution under the microscope within 3-5 days of germination. We quantify their shape by a novel computational pipeline that can automatically phenotype morphology. Our phenotyping pipeline quantifies traits like length, curvature, perimeter, area, and ‘hooking.’ Our objective is to quantify their response to nutrient stress to determine the function of hooked hairs in common bean during early development. We used the pipeline for analyzing our dataset of hydroponically grown beans and observed statistically significant responses compared to the control for length, curvature, perimeter, and area to nitrogen (p<0.001**) and phosphorus (p<0.001**) stress treatments. The calculation of ‘hooking’ for our dataset is still ongoing. We are simultaneously developing a landmark-free method for the two-dimensional shape analysis of our dataset and believe that our phenotyping efforts will enable the high-throughput analysis of morphological root hair traits for any plant species.

Understanding root traits is essential to improve water uptake, increase nitrogen capture and raise carbon sequestration from the atmosphere. However, high-throughput phenotyping to quantify root traits for deeper field-grown roots remain a challenge. Recently developed open-source methods use image-based 3D reconstruction algorithms to build 3D models of plant roots from multiple 2D images and can extract root traits and phenotypes. Most of these methods rely on automated image orientation (Structure from Motion)[1] and dense image matching (Multiple View Stereo) algorithms to produce a 3D point cloud or mesh model from 2D images. Until now it is not known how the performance of these methods compares to each other when applied to field-grown roots. We investigate commonly used open-source pipelines on a test panel of twelve contrasting maize genotypes grown in real field conditions in this comparison study [2-6]. We compare 3D point clouds in terms of number of points, computation time, and model surface density. This comparison study will provide insight into the performance of different open-source pipelines for maize root phenotyping, and illuminates trade-offs between 3D model quality and performance cost for future high-throughput 3D root phenotyping.

Understanding root traits is essential to improve water uptake, increase nitrogen capture and accelerate carbon sequestration from the atmosphere. High-throughput phenotyping to quantify root traits for deeper field-grown roots remains a challenge, however. Recently developed open-source methods use 3D reconstruction algorithms to build 3D models of plant roots from multiple 2D images and can extract root traits and phenotypes. Most of these methods rely on automated image orientation (Structure from Motion)[1] and dense image matching (Multiple View Stereo) algorithms to produce a 3D point cloud or mesh model from 2D images. Until now the performance of these methods when applied to field-grown roots has not been compared tested commonly used open-source pipelines on a test panel of twelve contrasting maize genotypes grown in real field conditions[2-6]. We compare the 3D point clouds produced in terms of number of points, computation time and model surface density. This comparison study provides insight into the performance of different open-source pipelines for maize root phenotyping and illuminates trade-offs between 3D model quality and performance cost for future high-throughput 3D root phenotyping.

We quantify the shape of hooked hairs which is a newly observed phenotype of epidermal cell extensions [1] in the common bean genotype L88-57 (Phaseolus vulgaris). The hooked hairs emerge below-ground before the root hairs and have a distinct ‘hooking’ morphology. We generated a dataset capturing their full distribution under the microscope within 3-5 days of germination. We quantify their shape by a novel computational pipeline that can automatically phenotype morphology. Our phenotyping pipeline quantifies traits like length, curvature, perimeter, area, and ‘hooking.’ Our objective is to quantify the response of hooked hairs to nutrient-stress to determine their function in common bean during early development. We used the pipeline for analyzing our dataset of hydroponically grown beans and observed statistically significant responses compared to the control for length, curvature, perimeter, and area to nitrogen (p<0.001**) and phosphorus (p<0.001**) stress treatments. The calculation of ‘hooking’ for our dataset is still ongoing. We are simultaneously developing a landmark-free method for the two-dimensional shape analysis of our dataset and believe that our phenotyping efforts will enable the high-throughput analysis of morphological root hair traits for any plant species.

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.