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. 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 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 to 3600 (DIRT/3D [VisualSFM‐based 3D reconstruction]) to around 360 for computational test 1, and around 600 for computational test 2 (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 problems. The updated DIRT/3D (COLMAP‐based 3D reconstruction) pipeline enables quicker image collection without compromising the accuracy of 3D root‐trait measurements.
Comparison of open-source image-based reconstruction pipelines for 3D root phenotyping of field-grown maize
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.
DOI recognition was not working: https://doi.org/10.1002/essoar.10508794.2
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- Award ID(s):
- 1845760
- NSF-PAR ID:
- 10320170
- Editor(s):
- Bucksch, Alexander Clarke
- Date Published:
- Journal Name:
- 2022 NAPPN Conference Proceedings
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
Improving root traits to improve efficiency of nutrient uptake in plants is an opportunity to increase crop production in response to climate change induced edaphic stresses. Maize (Zea mays L.) studies showed a large variation of root architecture traits in response to such stresses. Quantifying this response uses highthroughput, image-based phenotyping to characterize root architecture variation across edaphic stresses. Our objective is to test if commonly used root traits discriminate stress environments and if a single mathematical description of the complete root architecture reveals a phenotypic spectrum of root architectures in the B73 maize line using manual, DIRT/2D (Digital Imaging of Root Traits) and DIRT/3D measurements. Maize B73 inbred lines were grown in three field conditions: nonlimiting conditions, high nitrogen (N), and low N. A proprietary 3D scanner captured 2D and 3D images of harvested maize roots to compute root descriptors that distinguish shapes of root architecture. The results showed that the normalized mean value of computational root traits from DIRT/2D and DIRT/3D indicated significant discrimination among B73 across environments. We found a strong correlation (R2> 0.8) between the traits measured in 3D point clouds and manually measured traits. Ear weight and shoot biomass in low N significantly decreased by 45% and 21%, respectively. Low N reduced the maximum root system diameter by 13%, root system diameter by 10%, and root system length by 9%. The 2D and 3D whole root descriptors distinguished three different root architectural shapes of B73 in the same field. Our study assists plant breeders to improve crop productivity and stress tolerance in maize.more » « less
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Challenge: Digital Imaging of root traits 3D (DIRT/3D) [1] is a software to measure 3D root traits on excavated roots crowns from the field. However, quantifying 3D root traits remains a challenge due to the unknown tradeoff between 3D root-model quality and 3D root-trait accuracy [2]. Questions: Can the 3D root model reconstruction be improved while reducing the image-capturing effort? Does improved 3D root model quality increase the accuracy of trait measurements? Evaluation: Compare reconstruction performance of five open-source 3D model reconstruction pipelines on 12 architecturally contrasting genotypes [1] of field-grown maize roots. Evaluate the accuracy of 3D root traits between the original implementation of DIRT/3D based on VisualSFM with an implementation based on COLMAP. Conclusion: The updated DIRT/3D (COLMAP) pipeline enables quicker image collection by reducing the number of images needed and reducing the human factor during image collection. The results demonstrate that the accuracy of 3D root-trait measurements remained uncompromised.more » « less
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Abstract Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture. Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.more » « less
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Abstract The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.more » « less
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- Conference Paper:
- https://doi.org/10.1002/essoar.10508794.2
