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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Ultra-Wideband Microwave Imaging System for Root Phenotyping

The roots are a vital organ for plant growth and health. The opaque surrounding environment of the roots and the complicated growth process means that in situ and non-destructive root phenotyping face great challenges, which thus spur great research interests. The existing methods for root phenotyping are either unable to provide high-precision and high accuracy in situ detection, or they change the surrounding root environment and are destructive to root growth and health. Thus,we propose and develop an ultra-wideband microwave scanning method that uses time reversal to achieve in situ root phenotyping nondestructively. To verify the method’s feasibility, we studied an electromagnetic numerical model that simulates the transmission signal of two ultra-wideband microwave antennas. The simulated signal of roots with different shapes shows the proposed system’s capability to measure the root size in the soil. Experimental validations were conducted considering three sets of measurements with different sizes, numbers and locations, and the experimental results indicate that the developed imaging system was able to differentiate root sizes and numbers with high contrast. The reconstruction from both simulations and experimental measurements provided accurate size estimation of the carrots in the soil, which indicates the system’s potential for root imaging.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multi-objective optimization of root phenotypes for nutrient capture using evolutionary algorithms

Root phenotypes are avenues to the development of crop cultivars with improved nutrient capture, which is an important goal for global agriculture. The fitness landscape of root phenotypes is highly complex and multidimensional. It is difficult to predict which combinations of traits (phene states) will create the best performing integrated phenotypes in various environments. Brute force methods to map the fitness landscape by simulating millions of phenotypes in multiple environments are computationally challenging. Evolutionary optimization algorithms may provide more efficient avenues to explore high dimensional domains such as the root phenotypic space. We coupled the three-dimensional functional–structural plant model, SimRoot, to the Borg Multi-Objective Evolutionary Algorithm (MOEA) and the evolutionary search over several generations facilitated the identification of optimal root phenotypes balancing trade-offs across nutrient uptake, biomass accumulation, and root carbon costs in environments varying in nutrient availability. Our results show that several combinations of root phenes generate optimal integrated phenotypes where performance in one objective comes at the cost of reduced performance in one or more of the remaining objectives, and such combinations differed for mobile and non-mobile nutrients and for maize (a monocot) and bean (a dicot). Functional–structural plant models can be used with multi-objective optimization to identify optimal root phenotypes under various environments, including future climate scenarios, which will be useful in developing the more resilient, efficient crops urgently needed in global agriculture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Root phenotypes for improved nitrogen capture

Abstract Background Suboptimal nitrogen availability is a primary constraint for crop production in low-input agroecosystems, while nitrogen fertilization is a primary contributor to the energy, economic, and environmental costs of crop production in high-input agroecosystems. In this article we consider avenues to develop crops with improved nitrogen capture and reduced requirement for nitrogen fertilizer. Scope Intraspecific variation for an array of root phenotypes has been associated with improved nitrogen capture in cereal crops, including architectural phenotypes that colocalize root foraging with nitrogen availability in the soil; anatomical phenotypes that reduce the metabolic costs of soil exploration, improve penetration of hard soil, and exploit the rhizosphere; subcellular phenotypes that reduce the nitrogen requirement of plant tissue; molecular phenotypes exhibiting optimized nitrate uptake kinetics; and rhizosphere phenotypes that optimize associations with the rhizosphere microbiome. For each of these topics we provide examples of root phenotypes which merit attention as potential selection targets for crop improvement. Several cross-cutting issues are addressed including the importance of soil hydrology and impedance, phenotypic plasticity, integrated phenotypes, in silico modeling, and breeding strategies using high throughput phenotyping for co-optimization of multiple phenes. Conclusions Substantial phenotypic variation exists in crop germplasm for an array of root phenotypes that improve nitrogen capture. Although this topic merits greater research attention than it currently receives, we have adequate understanding and tools to develop crops with improved nitrogen capture. Root phenotypes are underutilized yet attractive breeding targets for the development of the nitrogen efficient crops urgently needed in global agriculture.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

RootSlice —A novel functional-structural model for root anatomical phenotypes

Root anatomy is an important determinant of root metabolic costs, soil exploration, and soil resource capture. Root anatomy varies substantially within and among plant species. RootSlice is a multicellular functional-structural model of root anatomy developed to facilitate the analysis and understanding of root anatomical phenotypes. RootSlice can capture phenotypically accurate root anatomy in three dimensions of different root classes and developmental zones, of both monocotyledonous and dicotyledonous species. Several case studies are presented illustrating the capabilities of the model. For maize nodal roots, the model illustrated the role of vacuole expansion in cell elongation; and confirmed the individual and synergistic role of increasing root cortical aerenchyma and reducing the number of cortical cell files in reducing root metabolic costs. Integration of RootSlice for different root zones as the temporal properties of the nodal roots in the whole-plant and soil model OpenSimRoot/maize enabled the multiscale evaluation of root anatomical phenotypes, highlighting the role of aerenchyma formation in enhancing the utility of cortical cell files for improving plant performance over varying soil nitrogen supply. Such integrative in silico approaches present avenues for exploring the fitness landscape of root anatomical phenotypes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

DIRT/3D: 3D root phenotyping for field-grown maize ( Zea mays )

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 r 2 > 0.84 and a high broad-sense heritability of H$_{mean}^{2}$ < 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.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Comparison of open‐source three‐dimensional reconstruction pipelines for maize‐root phenotyping

Abstract 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.

09 BIOMASS FUELS↗
DOE OSTI2022

Installation and imaging of thousands of minirhizotrons to phenotype root systems of field-grown plants

Roots are vital to plant performance because they acquire resources from the soil and provide anchorage. However, it remains difficult to assess root system size and distribution because roots are inaccessible in the soil. Existing methods to phenotype entire root systems range from slow, often destructive, methods applied to relatively small numbers of plants in the field to rapid methods that can be applied to large numbers of plants in controlled environment conditions. Much has been learned recently by extensive sampling of the root crown portion of field-grown plants. But, information on large-scale genetic and environmental variation in the size and distribution of root systems in the field remains a key knowledge gap. Minirhizotrons are the only established, non-destructive technology that can address this need in a standard field trial. Prior experiments have used only modest numbers of minirhizotrons, which has limited testing to small numbers of genotypes or environmental conditions. This study addressed the need for methods to install and collect images from thousands of minirhizotrons and thereby help break the phenotyping bottleneck in the field. Over three growing seasons, methods were developed and refined to install and collect images from up to 3038 minirhizotrons per experiment. Modifications were made to four tractors and hydraulic soil corers mounted to them. High quality installation was achieved at an average rate of up to 84.4 minirhizotron tubes per tractor per day. A set of four commercially available minirhizotron camera systems were each transported by wheelbarrow to allow collection of images of mature maize root systems at an average rate of up to 65.3 tubes per day per camera. This resulted in over 300,000 images being collected in as little as 11 days for a single experiment. The scale of minirhizotron installation was increased by two orders of magnitude by simultaneously using four tractor-mounted, hydraulic soil corers with modifications to ensure high quality, rapid operation. Image collection can be achieved at the corresponding scale using commercially available minirhizotron camera systems. Along with recent advances in image analysis, these advances will allow use of minirhizotrons at unprecedented scale to address key knowledge gaps regarding genetic and environmental effects on root system size and distribution in the field.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Improving 3D reconstruction quality for root phenotyping: assessing the impact of camera calibration and imaging parameters

Arate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracyccu remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding in downstream analysis.

3D reconstruction↗
DOE OSTI2020

Methods and devices for non-invasive root phenotyping

The present disclosure provides for an electronic sensor for detecting a root of a plant in soil, the electronic sensor that includes a first conductor plate configured to be disposed in soil, a switch, a power supply, and signal extractor. The switch is electrically coupled to the first conductor plate and is configured to switch between a first mode and a second mode. The power supply is electrically coupled to the switch and is configured to provide an electrical charge to the first conductor plate in the first mode of the switch. The signal extractor is electrically coupled to the switch and is configured to extract a signal response at the first conductor plate in the second mode of the switch. The present disclosure further provides a second conductor plate configured to be disposed in soil adjacent to and substantially parallel to the first conductor plate. The second conductor plate is electrically coupled to ground.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Methods and devices for non-invasive root phenotyping

The present disclosure provides for an electronic sensor for detecting a root of a plant in soil, the electronic sensor that includes a first conductor plate configured to be disposed in soil, a switch, a power supply, and signal extractor. The switch is electrically coupled to the first conductor plate and is configured to switch between a first mode and a second mode. The power supply is electrically coupled to the switch and is configured to provide an electrical charge to the first conductor plate in the first mode of the switch. The signal extractor is electrically coupled to the switch and is configured to extract a signal response at the first conductor plate in the second mode of the switch. The present disclosure further provides a second conductor plate configured to be disposed in soil adjacent to and substantially parallel to the first conductor plate. The second conductor plate is electrically coupled to ground.

Benfey, Philip N.↗
DOE OSTI2023

Methods and devices for non-invasive root phenotyping

The present disclosure provides for an electronic sensor for detecting a root of a plant in soil, the electronic sensor that includes a first conductor plate configured to be disposed in soil, a switch, a power supply, and a signal extractor. The switch is electrically coupled to the first conductor plate and is configured to switch between a first mode and a second mode. The power supply is electrically coupled to the switch and is configured to provide an electrical charge to the first conductor plate in the first mode of the switch. The signal extractor is electrically coupled to the switch and is configured to extract a signal response at the first conductor plate in the second mode of the switch. The present disclosure further provides a second conductor plate configured to be disposed in soil adjacent to and substantially parallel to the first conductor plate. The second conductor plate is electrically coupled to ground.

Benfey, Philip↗
DOE OSTI2022

Bioenergy Underground: Challenges and opportunities for phenotyping roots and the microbiome for sustainable bioenergy crop production

Abstract Bioenergy production often focuses on the aboveground feedstock production for conversion to fuel and other materials. However, the belowground component is crucial for soil carbon sequestration, greenhouse gas fluxes, and ecosystem function. Roots maximize feedstock production on marginal lands by acquiring soil resources and mediating soil ecosystem processes through interactions with the microbial community. This belowground world is challenging to observe and quantify; however, there are unprecedented opportunities using current methodologies to bring roots, microbes, and soil into focus. These opportunities allow not only breeding for increased feedstock production but breeding for increased soil health and carbon sequestration as well. A recent workshop hosted by the USDOE Bioenergy Research Centers highlighted these challenges and opportunities while creating a roadmap for increased collaboration and data interoperability through standardization of methodologies and data using F.A.I.R. principles. This article provides a background on the need for belowground research in bioenergy cropping systems, a primer on root system properties of major U.S. bioenergy crops, and an overview of the roles of root chemistry, exudation, and microbial interactions on sustainability. Crucially, we outline how to adopt standardized measures and databases to meet the most pressing methodological needs to accelerate root, soil, and microbial research to meet the pressing societal challenges of the century.

09 BIOMASS FUELS↗
DOE OSTI2022

Design and demonstration of a low‐field magnetic resonance imaging rhizotron for in‐field imaging of energy sorghum roots

Abstract Root phenotyping provides critical information to plant breeders for developing varieties with improved drought tolerance, greater root biomass, and greater nutrient use efficiency. Phenotyping roots in the natural environment is important for understanding the effect of the soil environment on root genotypic expressions. The goal of this work was to design and test a field‐scale mobile low‐field magnetic resonance imaging (LF‐MRI) Rhizotron that produces actionable root phenotyping data. We demonstrated this novel technology for root visualization and quantification using a LF‐MRI Rhizotron operating at 47 mT with two soil types. The LF‐MRI Rhizotron weights 453 kg, with a height of 90 cm, a diameter of 28 cm and an imaging field of view of 28 cm × 28 cm. The unit was operated in a Belk clay (Entic Hapluderts) and Weswood silt loam (Udifluventic Halustepts) generating 2‐D and 3‐D image data sets. The 2‐D image data had a collection time of 16.5 min per image at an image resolution of 2.2 mm per pixel. The 3‐D data had a collection time of 13 h per image with a 2.2 × 2.2 × 2.2 mm voxel resolution. Low‐field magnetic resonance imaging worked well for visualizing roots in moderate to high clay soils, demonstrating the potential for this technology; however, the broad application of this platform is hampered due to the prohibitively long scanning time to obtain 3‐D images. By increasing the field strength, and therefore the signal‐to‐noise ratio, faster scan times can enable a more useful system for root phenotyping.

Bagnall, G. Cody↗
DOE OSTI2022

Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.)

Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Plant Growth Environments with Programmable Relative Humidity and Homogeneous Nutrient Availability

We describe the design, characterization, and use of “programmable”, sterile growth environments for individual (or small sets of) plants. The specific relative humidities and nutrient availability experienced by the plant is established (RH between 15% and 95%; nutrient concentration as desired) during the setup of the growth environment, which takes about 5 minutes and <1$ in disposable cost. These systems maintain these environmental parameters constant for at least 14 days with minimal intervention (one minute every two days). The design is composed entirely of off-the-shelf components (e.g., LEGO1 bricks) and is characterized by (i) a separation of root and shoot environment (which is physiologically relevant and facilitates imposing specific conditions on the root system, e.g., darkness), (ii) the development of the root system on a flat surface, where the root enjoys constant contact with nutrient solution and air, (iii) a compatibility with root phenotyping. We demonstrate phenotyping by characterizing root systems of Brassica rapa plants growing in different relative humidities (55%, 75%, and 95%). While most phenotypes were found to be sensitive to these environmental changes, a phenotype tightly associated with root system topology– the size distribution of the areas encircled by roots–appeared to be remarkably and counterintuitively insensitive to humidity changes. These setups combine many of the advantages of hydroponics conditions (e.g., root phenotyping, complete control over nutrient composition, scalability) and soil conditions (e.g., aeration of roots, shading of roots), while being comparable in cost and setup time to Magenta1 boxes.

54 ENVIRONMENTAL SCIENCES↗