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1. TESTING THE THEORY OF THE PHENOTYPIC SPECTRUM ON 3-DIMENSIONAL ROOT ARCHITECTURE OF MAIZE

   Jitrana Kengkanna, Molly Hanlon ( June 2022 , Crops 2022)

   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 bymore »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.« less
2. Quantification of Phenotypic Responses to Root-Root Interactions Among Common Beans in a Specialized Mesocosm

   William LaVoy, Limeng Xie ( June 2022 , CROPS 2022)

   Root-root interactions alter the architectural organization of individual root systems and therefore, affect nutrient foraging (O’Brien et al., 2005). Past reports have shown detrimental and beneficial effects to the amount of yield in crops as they avoid or prefer belowground competition (Li et al., 2006; O’Brien et al., 2005). With little research done into root-root interactions there is still much to discover about the root phenotypes arising from root-root interactions and functions. Quantifying architectural traits of root system interactions would provide insight for researchers into the benefit of a cooperation vs. competition trade-off belowground. We have begun to develop a soil filled mesocosm system to perform a series of preliminary studies using 3D imaging to develop metrics of root-root interaction using common beans (Phaseolus vulgaris). Common beans have a relatively fast growing and sparse adventitious and basal root system, making them a suitable organism for this imaging study. Our second revision of the mesocosm focused on improving and fine tuning a mesh system that provides better support for the root architecture during the soil removal process. We use a light-weight, low-visibility plastic mesh originally used as bird netting to allow image capture from all sides. Traits that we aim tomore »extract include root growth angle, rooting depth, and root volume relative to neighbors, because these spatial qualities determine the soil areas that the root system will be foraging in. Our data will allow for the quantification and association of root plasticity in the presence of belowground competition.« less
3. TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging

   https://doi.org/10.1186/s13007-021-00829-z  

   Zeng, Dan ; Li, Mao ; Jiang, Ni ; Ju, Yiwen ; Schreiber, Hannah ; Chambers, Erin ; Letscher, David ; Ju, Tao ; Topp, Christopher N. ( December 2021 , Plant Methods)

   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 rangemore »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.« less
4. Comparison of open-source image-based reconstruction pipelines for 3D root phenotyping of field-grown maize

   https://doi.org/10.1002/essoar.10508794.2  

   Liu, Suxing Bonelli ( February 2022 , 2022 NAPPN Conference Proceedings)

   Bucksch, Alexander Clarke (Ed.)

   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
5. A METHOD TO GENERATE FLOOD MAPS IN 3D USING DEM AND DEEP LEARNING

   https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-25-2020  

   Gebrehiwot, A. ; Hashemi-Beni, L. ( January 2020 , ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)

   Abstract. High-resolution remote sensing imagery has been increasingly used for flood applications. Different methods have been proposed for flood extent mapping from creating water index to image classification from high-resolution data. Among these methods, deep learning methods have shown promising results for flood extent extraction; however, these two-dimensional (2D) image classification methods cannot directly provide water level measurements. This paper presents an integrated approach to extract the flood extent in three-dimensional (3D) from UAV data by integrating 2D deep learning-based flood map and 3D cloud point extracted from a Structure from Motion (SFM) method. We fine-tuned a pretrained Visual Geometry Group 16 (VGG-16) based fully convolutional model to create a 2D inundation map. The 2D classified map was overlaid on the SfM-based 3D point cloud to create a 3D flood map. The floodwater depth was estimated by subtracting a pre-flood Digital Elevation Model (DEM) from the SfM-based DEM. The results show that the proposed method is efficient in creating a 3D flood extent map to support emergency response and recovery activates during a flood event.