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.

Quantifying phenotypes of root-root interactions would allow a greater understanding of how plants react to belowground competition through plasticity of architectural traits. Past research has shown that plants will over proliferate roots in the presence of competition, leaving less resources to allocate above ground, negatively impacting shoot growth and yields [1]. Further evidence highlights plants may recognize kin and non-kin relationships, responding by avoiding competing with close relatives and instead allocating root mass closer to non-kin neighbors [2]. In an attempt to visualize and quantify root architecture plasticity involved in these root-root interactions, we developed a modified mesocosm system. Within the mesocosm box common bean seeds were germinated 10 inches apart from each other. Mesh screens were placed on either side of each bean, in order to capture root growth towards each other and/or away from each other. Two treatments were involved, with mesocosms grown with either kin or non-kin plants. Plants will be harvested at the 6-week mark, when the root archetype will be developed and prominent. During harvesting one side of the mesocosm box will be removed and the soil will be washed away. We will quantify the degree of root competition by counting the number of roots passing the mesh screen. All general 2D root traits will be measured in DIRT2D. This experiment will shine light on an understudied section of crop science and will allow farmers and researchers a better understanding of an otherwise unseen phenomena.

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.

(250 words) Micronutrients, such as iron, zinc, and sulfur, play a vital role in both plant and human development. Understanding how plants sense and allocate nutrients within their tissues may offer different venues to develop plants with high nutritional value. Despite decades of intensive research, more than 40% of genes in Arabidopsis remain uncharacterized or have no assigned function. While several resources such as mutant populations or diversity panels offer the possibility to identify genes critical for plant nutrition, the ability to consistently track and assess plant growth in an automated, unbiased way is still a major limitation. High-throughput phenotyping (HTP) is the new standard in plant biology but few HTP systems are open source and user friendly. Therefore, we developed OPEN Leaf, an open source HTP for hydroponic experiments. OPEN Leaf is capable of tracking changes in both size and color of the whole plant and specific regions of the rosette. We have also integrated communication platforms (Slack) and cloud services (CyVerse) to facilitate user communication, collaboration, data storage, and analysis in real time. As a proof-of-concept, we report the ability of OPEN Leaf to track changes in size and color when plants are growing hydroponically with different levels of nutrients. We expect that the availability of open source HTP platforms, together with standardized experimental conditions agreed by the scientific community, will advance the identification of genes and networks mediating nutrient uptake and allocation in plants.

Continuous collection and analysis of high-resolution phenotype data is critical to develop crops resilient to the consequences of climate change. Though web-accessible tools for parallel, reproducible scientiSic workSlows render big data increasingly tractable, software for plant science remains inadequate for large-scale precision agriculture. Cyberinfrastructure must present minimal barriers to entry, accommodate rapidly changing dependencies, support a wide variety of use cases, and weave together sensors at the edge, laptops, clusters, and cloud storage into a coherent virtual workspace. PlantIT is a web portal intended as such an environment. Platforms like PlantIT and its precursor DIRT [1] permit efSicient phenotyping and equip geographically distributed researchers with a code-optional interface. WorkSlows are published in Docker images, deployed as Singularity containers to public or private computing resources, and monitored in real time. Data are stored automatically in the CyVerse Data Store and can be annotated according to the MIAPPE [2] standard. GitHub integration provides versioning and repositories can be activated with a single conSiguration Sile, like Travis or GitHub Actions. Containers allow for a range of use cases, including image-based trait measurements, 3D reconstructions, morphological growth simulations, and crop modeling. Pseudo-batch/stream processing is also necessary; as data scales, manual batch jobs rapidly become infeasible, and (re-)analysis must occur upon arrival in near-real-time. We suggest web-accessible phenotyping automation software may address bottlenecks and help reveal undiscovered relationships between genes, traits, and the environment.