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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

3D-convolution neural networks↗
DOE OSTI2025

Microbial O 2 consumption as a function of pore structure in soils of sorghum, switchgrass, and prairie vegetation systems

Abstract Microscale O 2 availability in soil, a main factor influencing microbial processing of soil carbon and nitrogen, is a function of O 2 diffusion and microbial O 2 consumption. While O 2 diffusivity is widely studied, it is difficult to quantify the rate of microbial O 2 consumption ( MO ). In this study, we quantified the MO using O 2 microsensor profiling to compare MO in different soils and vegetation systems while manipulating the soil pore structure. Soil samples were collected from three bioenergy vegetation systems, sorghum ( Sorghum bicolor L. Moench), switchgrass ( Panicum virgatum L.), and prairie, at an experimental site in Southeast Michigan, and from switchgrass and prairie systems at two sites in South and North Wisconsin. We prepared soil cores with two contrasting pore structures, that is, large (>30 µm Ø) pore‐dominated soil (LP) and small (<10 µm Ø) pore‐dominated soil (SP), from the same 1‐ to 2‐mm soil size fraction. We quantified MO (nmol O 2 cm −3 s −1 ) using a microprofiler with an oxygen (OX‐100 µm) microsensor (Unisense) pre‐ and post‐38‐day incubation. Across all studied systems, there was a trend for a decrease in SP MO post‐incubation, suggesting a change in microbial metabolism to lower O 2 utilization. However, sizeable microbial O 2 consumption continued in the LP soils, further corroborated by a positive correlation between the C‐CO 2 emission rate and the post‐incubation MO , highlighting that LP provided a better physical microenvironment for the soil microbes.

Chakraborty, Poulamee [Department of Plant Soil an↗
DOE OSTI2026

A sorghum pangenome reference improves global crop trait discovery

Although the green revolution adapted a handful of crops to homogeneous and high-input industrialized agriculture, much of the global population still relies on the local production of variable crop cultivars by low-input smallholder farms. This diversity of unhomogenized crops, like that of the grain and bioenergy crop sorghum, offers raw materials for genetic gain and cultivar improvement. However, breeding efforts can be constrained by highly specialized traits and breeding targets Here, to bridge this diversity, we constructed a 33-member pangenome reference and a diversity panel across 1,984 cultivars and landraces. We leveraged these resources to explore the complex interplay among historical contingency, ongoing adaptation and previously uncharacterized structural diversity. Specifically, our analyses conclusively demonstrated multiple nested and deeply diverged structural variants in the domestication gene SHATTERING1, which distinguish the previously established multicentric origin of sorghum. We then applied landscape genomics to reveal how gene flow and secondary contact created the complex genetic mosaic in contemporary breeding networks. As proof of concept for pangenome-accelerated trait discovery, we connected biosynthetic gene cluster structural variation to phenotypic leaf concentration of the cyanogenic glucoside dhurrin. Combined, these approaches will accelerate breeding and trait discovery and provide a framework for similar applications in other crops.

agricultural genetics↗
DOE OSTI2021

The inheritance of anthracnose (Colletotrichum sublineola) resistance in sorghum differential lines QL3 and IS18760

Anthracnose caused by the fungal pathogen C. sublineola is an economically important constraint on worldwide sorghum production. The most effective strategy to safeguard yield is through the introgression of resistance alleles. This requires elucidation of the genetic basis of the different resistance sources that have been identified. In this study, 223 recombinant inbred lines (RILs) derived from crossing anthracnose-differentials QL3 (96 RILs) and IS18760 (127 RILs) with the common susceptible parent PI609251 were evaluated at four field locations in the United States (Florida, Georgia, Texas, and Puerto Rico) for their anthracnose resistance response. Both RIL populations were highly susceptible to anthracnose in Florida and Georgia, while in Puerto Rico and Texas they were segregating for anthracnose resistance response. A genome scan using a composite linkage map of 982 single nucleotide polymorphisms (SNPs) detected two genomic regions of 4.31 and 0.85 Mb on chromosomes 4 and 8, respectively, that explained 10–27% of the phenotypic variation in Texas and Puerto Rico. In parallel, a subset of 43 RILs that contained 67% of the recombination events were evaluated against anthracnose pathotypes from Arkansas (2), Puerto Rico (2) and Texas (4) in the greenhouse. A genome scan showed that the 7.57 Mb region at the distal end of the short arm of chromosome 5 is associated with the resistance response against the pathotype AMP-048 from Arkansas. Comparative analysis identified the genomic region on chromosome 4 overlaps with an anthracnose resistance locus identified in another anthracnose-differential line, SC414-12E, indicating this genomic region is of interest for introgression in susceptible sorghum germplasm. Candidate gene analysis for the resistance locus on chromosome 5 identified an R -gene cluster that has high similarity to another R -gene cluster associated with anthracnose resistance on chromosome 9.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A sorghum ascorbate peroxidase with four binding sites has activity against ascorbate and phenylpropanoids

Abstract In planta, H2O2 is produced as a by-product of enzymatic reactions and during defense responses. Ascorbate peroxidase (APX) is a key enzyme involved in scavenging cytotoxic H2O2. Here, we report the crystal structure of cytosolic APX from sorghum (Sorghum bicolor) (Sobic.001G410200). While the overall structure of SbAPX was similar to that of other APXs, SbAPX uniquely displayed four bound ascorbates rather than one. In addition to the ɣ-heme pocket identified in other APXs, ascorbates were bound at the δ-meso and two solvent-exposed pockets. Consistent with the presence of multiple binding sites, our results indicated that the H2O2-dependent oxidation of ascorbate displayed positive cooperativity. Bound ascorbate at two surface sites established an intricate proton network with ascorbate at the ɣ-heme edge and δ-meso sites. Based on crystal structures, steady-state kinetics, and site-directed mutagenesis results, both ascorbate molecules at the ɣ-heme edge and the one at the surface are expected to participate in the oxidation reaction. We provide evidence that the H2O2-dependent oxidation of ascorbate by APX produces a C2-hydrated bicyclic hemiketal form of dehydroascorbic acid at the ɣ-heme edge, indicating two successive electron transfers from a single-bound ascorbate. In addition, the δ-meso site was shared with several organic compounds, including p-coumaric acid and other phenylpropanoids, for the potential radicalization reaction. Site-directed mutagenesis of the critical residue at the ɣ-heme edge (R172A) only partially reduced polymerization activity. Thus, APX removes stress-generated H2O2 with ascorbates, and also uses this same H2O2 to potentially fortify cell walls via oxidative polymerization of phenylpropanoids in response to stress.

Plant Sciences↗
DOE OSTI2026

Draft Genome Sequences of 14 Bacterial Isolates from the Rhizosphere of Bioenergy Sorghum

We report the draft genomes of a collection of 15 bacteria, isolated from the rhizosphere soil of bioenergy sorghum (Sorghum bicolor (L.) Moench). These isolates belong to the genera, Acidovorax, Nocardioides, Agrobacterium, Peribacillus, Caulobacter, Cupriavidus, Pseudomonas, Rhizobium, Sphingomonas, Priestia, Dyadobacter, Roseomonas, Ideonella, and Bacillus.

Black, Grace S.↗
DOE OSTI2025

TGCM: (T)rait, (G)ene, and (C)rop Growth (M)odel Directed Targeted Gene Characterization in Sorghum (Final Technical Report)

Understanding which genes control important crop traits could help scientists develop better bioenergy and food crops more efficiently. However, plant genomes contain tens of thousands of genes, and testing each one individually is expensive and time-consuming. This project developed computational tools to predict which genes are most likely to matter, allowing researchers to focus their efforts where they will have the greatest impact. This project developed and validated integrated approaches combining machine learning, quantitative genetics, and crop growth modeling to improve the efficiency of functional gene characterization in sorghum (Sorghum bicolor), a critical bioenergy and food security crop. The research addressed a fundamental challenge in plant biology: the majority of genes in plant genomes lack experimentally validated functions, making it difficult to prioritize which genes to study using resource-intensive reverse genetics approaches.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Biomass sorghum production in Iowa, USA

This dataset includes field measurement and model simulation data from Josh Bendorf's (former MS student with Drs. VanLoocke & Heaton, ISU Dept. of Agronomy) thesis project focusing on the simulation of biomass sorghum ( Sorghum bicolor L. Moench) in the state of Iowa. Measured field data are provided in CSV file format for 2019 & 2020. Model output files are provided as NetCDF file format for the state of Iowa (1998-2007) & the larger US region (1969-2018).

Agricultural production systems simulation↗
DOE OSTI2022

Dissecting the Genetic Architecture of Carbon Partitioning in Sorghum Using Multiscale Phenotypes

Carbon partitioning in plants may be viewed as a dynamic process composed of the many interactions between sources and sinks. The accumulation and distribution of fixed carbon is not dictated simply by the sink strength and number but is dependent upon the source, pathways, and interactions of the system. As such, the study of carbon partitioning through perturbations to the system or through focus on individual traits may fail to produce actionable developments or a comprehensive understanding of the mechanisms underlying this complex process. Using the recently published sorghum carbon-partitioning panel, we collected both macroscale phenotypic characteristics such as plant height, above-ground biomass, and dry weight along with microscale compositional traits to deconvolute the carbon-partitioning pathways in this multipurpose crop. Multivariate analyses of traits resulted in the identification of numerous loci associated with several distinct carbon-partitioning traits, which putatively regulate sugar content, manganese homeostasis, and nitrate transportation. Using a multivariate adaptive shrinkage approach, we identified several loci associated with multiple traits suggesting that pleiotropic and/or interactive effects may positively influence multiple carbon-partitioning traits, or these overlaps may represent molecular switches mediating basal carbon allocating or partitioning networks. Conversely, we also identify a carbon tradeoff where reduced lignin content is associated with increased sugar content. The results presented here support previous studies demonstrating the convoluted nature of carbon partitioning in sorghum and emphasize the importance of taking a holistic approach to the study of carbon partitioning by utilizing multiscale phenotypes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Sorghum epicuticular wax and aerial root mucilage microbiomes

Phyllosphere exudates select for distinct microbiome members in sorghum epicuticular wax and aerial root mucilage. We investigated the microbial communities associated with field-grown energy sorghum epicuticular wax and aerial root mucilage. Sequence data includes 16S rRNA amplicons and ITS amplicons.

Agriculture↗
NASA NTRS1981

A meteorologically driven grain sorghum stress indicator model

A grain sorghum soil moisture and temperature stress model is described. It was developed to serve as a meteorological data filter to alert commodity analysts to potential stress conditions and crop phenology in selected grain sorghum production areas. The model also identifies optimum conditions on a daily basis and planting/harvest problems associated with poor tractability.

Taylor, T. W.↗
NASA NTRS1987

Variations in the polarized leaf reflectance of Sorghum bicolor

The polarized reflectance factor, Rq, of sorghum (Sorghum bicolor, L.) leaves from field-grown plants was measured in situ in the summers of 1983 and 1984. In 1983, three leaves of two randomly selected plants were measured at 2-week intervals. The value of Rq varied, depending on leaf and day of measurement. Measured values of Rq for the adaxial leaf surface ranged from 16 to 53; for the abaxial leaf surface the values ranged from 28 to 69. In 1984, measurements consisted of repeated observations made on the same leaf at biweekly intervals. The values of Rq from the adaxial leaf surface ranged from 26 to 38. Values of Rq from the abaxial leaf surface increased throughout the season, from 16 to 45. Differences in Rq were attributed to changes in surface details of the leaf.

Grant, Lois↗
DOE OSTI2022

Elucidation of Triacylglycerol Overproduction in the C4 Bioenergy Crop Sorghum bicolor by Constraint-Based Analysis

Upregulation of triacylglycerols (TAGs) in vegetative plant tissues such as leaves has the potential to drastically increase the energy density and biomass yield of bioenergy crops. In this context, constraint-based analysis has the promise to improve metabolic engineering strategies. Here we present a core metabolism model for the C 4 biomass crop Sorghum bicolor ( iTJC1414 ) along with a minimal model for photosynthetic CO 2 assimilation, sucrose and TAG biosynthesis in C 3 plants. Extending iTJC1414 to a four-cell diel model we simulate C 4 photosynthesis in mature leaves with the principal photo-assimilatory product being replaced by TAG produced at different levels. Independent of specific pathways and per unit carbon assimilated, energy content and biosynthetic demands in reducing equivalents are about 1.3 to 1.4 times higher for TAG than for sucrose. For plant generic pathways, ATP- and NADPH-demands per CO 2 assimilated are higher by 1.3- and 1.5-fold, respectively. If the photosynthetic supply in ATP and NADPH in iTJC1414 is adjusted to be balanced for sucrose as the sole photo-assimilatory product, overproduction of TAG is predicted to cause a substantial surplus in photosynthetic ATP. This means that if TAG synthesis was the sole photo-assimilatory process, there could be an energy imbalance that might impede the process. Adjusting iTJC1414 to a photo-assimilatory rate that approximates field conditions, we predict possible daily rates of TAG accumulation, dependent on varying ratios of carbon partitioning between exported assimilates and accumulated oil droplets (TAG, oleosin) and in dependence of activation of futile cycles of TAG synthesis and degradation. We find that, based on the capacity of leaves for photosynthetic synthesis of exported assimilates, mature leaves should be able to reach a 20% level of TAG per dry weight within one month if only 5% of the photosynthetic net assimilation can be allocated into oil droplets. From this we conclude that high TAG levels should be achievable if TAG synthesis is induced only during a final phase of the plant life cycle.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Non-destructive, whole-plant phenotyping reveals dynamic changes in water use efficiency, photosynthesis, and rhizosphere acidification of sorghum accessions under osmotic stress

Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically stressed sorghum accessions. We identified relationships between root zone acidification and photosynthesis on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and should be suitable for any non-aquatic vascular plant species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Combining biotechnology with sustainability: feasibility of bioenergy sorghum in generating high-value bioproducts

Biofuels generated through fermentation of plant-derived lignocellulosic biomass are currently not competitive with fossil fuels. Here, a framework is described for the engineering of valuable coproducts to improve the economic feasibility of biofuel production. To accomplish this goal, the desirable traits of the bioenergy crop Sorghum bicolor are harnessed via modern engineering approaches. A robust analysis is provided to highlight the scientific basis, costs, regulatory challenges, and lucrative nature of coproduct development. Sustainable biofuel production could also have far-reaching societal impacts, especially in the context of our rapidly changing global climate. This perspective suggests that the path to profitable biofuel production is attainable and encourages consideration of similar approaches by the biotechnology industry.

09 BIOMASS FUELS↗