Biology subjects

Fahlgren, N.

Publications and source records attributed to Fahlgren, N..

12 recordsLinked to original sources

bioRxiv2026

Quantifying growth and lodging in Tef (Eragrostis tef) with Uncrewed Aerial Systems (UAS)

Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor-intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low-cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs. Significance StatementExtreme weather or heavy grain can cause plant stems to bend, a process called lodging. Lodging significantly reduces crop yields globally, particularly in grain crops such as tef (Eragrostis tef). Semidwarf crops have previously been reported to be lodging-resistant, increasing crop yields. Here, we used uncrewed aerial systems (UAS) to measure plant growth, height, and lodging in gene edited semidwarf tef lines, and compared the results to ground-truth data. Using a UAS equipped with a red-green-blue (RGB) camera or LiDAR sensor, we measured plant height and lodging, and found that early-season height measurements could predict future lodging potential. The tools used were contributed to the open-source software PlantCV-Geospatial for community use. This work contributes to a broader understanding of genetic resistance to lodging, providing valuable insights for tef crop improvement and reduces the need for labor-intensive manual measurements.

plant biology↗

bioRxiv2025

Leveraging High-dimensional Seed Colorspace in Phenomic Selection Models for Herbaceous Perennial Crops

Herbaceous perennial crops, which only recently entered the domestication pipeline, offer the potential of ecological benefits in agricultural systems but remain underdeveloped, partly due to slow breeding cycles. Early-stage phenomic selection, the process of using high-dimensional secondary traits to predict target traits, may accelerate improvement. We evaluated whether seed traits derived from image scans could be used to predict germination traits in three perennial crop candidate species: sainfoin (Onobrychis viciifolia), intermediate wheatgrass (Thinopyrum intermedium), and silflower (Silphium integrifolium). Over 20,000 seeds were scanned, and PlantCV was used to extract 692 hue, saturation, and value (HSV) color features along with five morphological traits. We demonstrated that seed color, morphology, and germination traits were all influenced by maternal family, but patterns of correlation between germination traits and color and morphology traits are complex. To enable prediction from high-dimensional seed traits, we constructed relationship matrices from combinations of HSV and morphology features for use in phenomic selection models to predict germination proportion and timing. Model performance varied by trait, species, and cohort, and in many cases, predictions were significantly better than chance. HSV features, when combined with morphology, often yielded the most accurate predictions, with performance reaching a maximum of r = 0.33. These results demonstrate the potential of low-cost, image-based phenomic data to inform early-stage selection and support the development of sustainable perennial crops. Plain language summaryMany of the worlds most important crops (e.g., wheat, maize, soy) live for less than one year, leaving soil bare for many months and subject to erosion. Longer-lived (perennial) alternatives to these crops have the potential to support a more sustainable agricultural system by improving soil health and reducing the need for chemical inputs. However, perennial herbaceous plants were not domesticated by early farmers, and now lag behind annual crops in development. They take longer to breed and often lack the genetic resources needed for modern breeding approaches. In this study, we tested whether simple seed images derived from color scanners could be used to predict key traits like germination proportion and timing in emerging perennial crops. We found that seed color and shape varied by maternal family and that color, especially when combined with seed size and shape, could help predict germination performance. These predictions were significantly better than chance. This work shows that low-cost, image-based seed data can support early-stage selection in breeding programs, especially for undeveloped perennial crops, helping save time, reduce cost, and accelerate progress toward more sustainable agriculture.

plant biology↗

bioRxiv2025

Redesigning petal shape, size, and color in soybean reveals unexpected phenotypes for floral organ development

Soybean (Glycine max) has not yet benefited from large-scale hybrid breeding efforts due to its small, self-fertilizing flowers that are difficult to emasculate, and limited attractiveness to pollinators. This study explores targeted floral trait engineering to enhance pollinator attraction, aiming to overcome barriers to soybean hybridization. We generated a high-resolution floral organ expression atlas and H3K4 trimethylation ChIP-Seq dataset to identify candidate genes involved in petal development, nectar sugar content, and petal pigmentation. Using CRISPR-based activation and repression systems, we modified the expression of AINTEGUMENTA (GmANT), BIGPETAL (GmBPE), and SUCROSE TRANSPORTER2 (GmSUC2). Contrary to expectations based on Arabidopsis homologs, transcriptional activation of GmANT_B and repression of GmBPE led to reduced, rather than increased, petal size, highlighting divergent regulatory mechanisms in soybean. Complementation of the W1 gene that controls petal pigmentation successfully converted white petals to purple, with preliminary evidence indicating that this color conversion may increase pollinator visitation. These results underscore the complexity of floral development in soybean and provide foundational tools and resources for future efforts to engineer reproductive traits for hybrid seed production.

plant biology↗

bioRxiv2022

Natural Variation in Brachypodium distachyon Responses to Combined Abiotic Stresses

The growing world population increases demand for agricultural production, which is more challenging as climate change increases global temperature and causes more extreme weather events. High-throughput phenotyping tools can be used to measure plant responses to the environment to identify genomic regions associated with response to stress. This study examines the phenotypic variation of 149 accessions of Brachypodium distachyon under drought, heat, and the combination of both stresses. Heat alone causes the largest amounts of tissue damage and the combination of heat and drought causes the largest decrease in plant biomass compared to other treatments. Notably, Bd21-0, the reference line for B. distachyon, was identified as not having very robust growth under stress conditions, especially in the heat-drought combined treatment. Climate data from the collection locations of these accessions (climate of origin) was used to assess whether climate of origin was correlated with responses to stresses and it was found to be significantly associated with height and percent of plant tissue damage. Additionally, genome wide association mapping found a number of genetic loci associated with changes in plant height, biomass, and the amount of damaged tissue under stress. Some SNPs found to be significantly associated with a response to heat or drought are also significantly associated in the combination of stresses, while others are not, and some significantly associated SNPs were only identified in the combined stress treatment. This, combined with the phenotypic data, indicates that the effects of these abiotic stresses are not simply additive, and the responses of B. distachyon to the combined stresses differ from drought and heat alone. Significant SNPs were closely located to genes known to be involved in plant responses to abiotic stresses.

plant biology↗

bioRxiv2021

Tasselyzer, a machine learning method to quantify anther extrusion in maize, based on PlantCV

Male fertility in maize involves complex genetic programming affected by environmental factors. Evaluating the presence and proportion of fertile anthers is crucial for agronomic purposes. Anthers in maize emerge from male-only florets, and quantifying anther exertion is a key indicator of male fertility; however, traditional manual scoring methods are subjective. To address this limitation, we developed an automated method, Tasselyzer, for large-scale analysis. This image-based program uses the PlantCV platform to provide a quantitative assessment of anther exertion, capturing regional differences within the tassel based on the distinct color of anthers. We successfully applied this method to diverse maize lines to demonstrate its utility for research and breeding programs. Significance StatementTasselyzer is a novel image-based segmentation tool for automated, large-scale measurement of anther exertion and the impact of genetic and environmental variation on male fertility in maize.

plant biology↗

bioRxiv2021

Large structural variations in the haplotype-resolved African cassava genome.

Cassava (Manihot esculenta Crantz, 2n=36) is a global food security crop. Cassava has a highly heterozygous genome, high genetic load, and genotype-dependent asynchronous flowering. It is typically propagated by stem cuttings and any genetic variation between haplotypes, including large structural variations, is preserved by such clonal propagation. Traditional genome assembly approaches generate a collapsed haplotype representation of the genome. In highly heterozygous plants, this results in artifacts and an oversimplification of heterozygous regions. We used a combination of Pacific Biosciences (PacBio), Illumina, and Hi-C to resolve each haplotype of the genome of a farmer-preferred cassava line, TME7 (Oko-iyawo). PacBio reads were assembled using the FALCON suite. Phase switch errors were corrected using FALCON-Phase and Hi-C read data. The ultra-long-range information from Hi-C sequencing was also used for scaffolding. Comparison of the two phases revealed more than 5,000 large haplotype-specific structural variants affecting over 8 Mb, including insertions and deletions spanning thousands of base pairs. The potential of these variants to affect allele specific expression was further explored. RNA-seq data from 11 different tissue types were mapped against the scaffolded haploid assembly and gene expression data are incorporated into our existing easy-to-use web-based interface to facilitate use by the broader plant science community. These two assemblies provide an excellent means to study the effects of heterozygosity, haplotype-specific structural variation, gene hemizygosity, and allele specific gene expression contributing to important agricultural traits and further our understanding of the genetics and domestication of cassava. Significance statementThe cassava varieties grown by subsistence farmers in Africa largely differ from the inbred reference genome due to their highly heterozygous nature. We used multiple sequencing technologies to assemble and resolve both haplotypes in TME7, a farmer-preferred cassava line, enabling us to study the considerable haplotypic structural variation in this line.

genomics↗

bioRxiv2019

Genomewide association study reveals transient loci underlying the genetic architecture of biomass accumulation under cold stress in Sorghum.

Sorghum bicolor is a promising cellulosic feedstock crop for bioenergy because of its potential for high biomass yields. However, in its early growth phases, sorghum is sensitive to cold stress, preventing early planting in temperate environments. Cold temperature adaptability is vital for the successful cultivation of both bioenergy and grain sorghum at higher latitudes and elevations, and for early season planting or to extend the growing season. Identification of genes and alleles that enhance biomass accumulation of sorghum grown under early cold stress would enable the development of improved bioenergy sorghum through breeding or genetic engineering. We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP is a collection of densely genotyped and racially, geographically, and phenotypically diverse accessions. The plants were weighed, watered, and imaged daily to measure growth dynamics and water use efficiency (WUE). Daily, non-destructive imaging allowed for a temporal analysis of growth-related traits in response to cold stress. We performed a genome-wide association study (GWAS) to identify candidate genomic intervals and genes controlling response to early cold stress. GWAS identified transient quantitative trait loci (QTL) strongly associated with each growth-related trait, permitting an investigation into the genetic basis of cold stress response at different stages of development. The analysis identified a priori and novel candidate genes associated with growth-related traits and the temporal response to cold stress.\n\nSIGNIFICANCE STATEMENTGenome-wide association study of bioenergy sorghum accessions phenotyped under early season cold stress revealed transient QTLs for highly heritable biomass and growth-related traits that appeared as the temperature increased and plants developed. Sorghum accessions clustered into multiple groups for each heritable trait with distinct growth profiles. GWAS identified candidate genes associated with growth traits and cold stress responses. The top-performing accessions with the highest growth-related trait values over time and temperature shifts will be useful for further genetic analysis and breeding or engineering efforts directed at biomass yield enhancements.

plant biology↗

bioRxiv2019

Heating quinoa shoots results in yield loss by inhibiting fruit production and delaying maturity

Increasing global temperatures and a growing world population create the need to develop crop varieties that yield more in warmer climates. There is growing interest in expanding quinoa cultivation, because of quinoas ability to produce nutritious grain in poor soils, with little water and at high salinity. However, the main limitation to expanding quinoa cultivation is quinoas susceptibility to temperatures above ~32{degrees}C. This study investigates the phenotypes, genes, and mechanisms that may affect quinoa seed yield at high temperatures. By using a differential heating system where only roots or only shoots were heated, quinoa yield losses were attributed to shoot heating. Plants with heated shoots lost 60% to 85% yield as compared to control. Yield losses were due to lower fruit production, which lowered the number of seeds produced per plant. Further, plants with heated shoots had delayed maturity and more non-reproductive shoot biomass, while plants with both heated roots and heated shoots produced more yield from panicles that escaped heat than control. This suggests that quinoa uses a type of avoidance strategy to survive heat. Gene expression analysis identified transcription factors differentially expressed in plants with heated shoots and low yield that had been previously associated with flower development and flower opening. Interestingly, in plants with heated shoots, flowers stayed closed during the day while control flowers were open. Although a closed flower may protect floral structures, this could also cause yield losses by limiting pollen dispersal, which is necessary to produce fruit in quinoas mostly female flowers.\n\nSignificance StatementThis study provides evidence that heating quinoa during flowering results in seed yield loss by lowering fruit production. Plants with low yield after heat treatment also matured more slowly, suggesting that quinoa may use a type of avoidance strategy to survive heat stress conditions. Genes differentially expressed under heat include genes involved in flower development and flower opening.

plant biology↗

bioRxiv2018

Antiviral Functions of ARGONAUTE Proteins During Turnip Crinkle Virus Infection Revealed by Image-based Trait Analysis in Arabidopsis

RNA-based silencing functions as an important antiviral immunity mechanism in plants. Plant viruses evolved to encode viral suppressors of RNA silencing (VSRs) that interfere with the function of key components in the silencing pathway. As effectors in the RNA silencing pathway, ARGONAUTE (AGO) proteins are targeted of by some VSRs, such as that encoded by Turnip crinkle virus (TCV). A VSR-deficient TCV mutant was used to identify AGO proteins with antiviral activities during infection. A quantitative phenotyping protocol using an image-based color trait analysis pipeline on the PlantCV platform, with temporal red, green and blue (RGB) imaging and a computational segmentation algorithm, was used to measure plant disease after TCV inoculation. This process captured and analyzed growth and leaf color of Arabidopsis plants in response to virus infection over time. By combining this quantitative phenotypic data with molecular assays to detect local and systemic virus accumulation, AGO2, AGO3, and AGO7 were shown to play antiviral roles during TCV infection. In leaves, AGO2 and AGO7 functioned as prominent non-additive, anti-TCV effectors, while AGO3 played a minor role. Other AGOs were required to protect inflorescence tissues against TCV. Overall, these results indicate that distinct AGO proteins have specialized, modular roles in antiviral defense across different tissues, and demonstrate the effectiveness of image-based phenotyping to quantify disease progression.\n\nAuthor SummaryPlant viruses caused substantial losses in crop production and quality worldwide. Precisely measuring plant health is critical for better understanding the mechanisms underlying plant virus and host interactions. Advances in high-resolution imaging technologies and deep-learning tools have made acquiring and analyzing \"big data\" of disease traits possible. In this study, we have developed a high-throughput, image-based trait phenotyping pipeline to quantify disease severity in Arabidopsis thaliana infected by Turnip Crinkle Virus (TCV). Our aim is to understand how the antiviral RNA silencing machinery is tuned to protect the host from invading virus infection. We focused on ARGONAUTE proteins, which are the effectors in the RNA silencing pathway. A mutant line of TCV with a dysfunctional silencing suppressor (P38) was used to investigate which ago mutation could compensate for the dysfunctional silencing suppressor and facilitate the development of disease symptoms. We demonstrated that specific AGO proteins contribute to protecting leaves from TCV infection in a non-additive manner. Our results also implied that distinct AGOs are required to function collectively to silence TCV in inflorescence tissues. More evidence is still needed to further understand how these antiviral AGOs interact with suppressor proteins molecularly during TCV infection.

plant biology↗

bioRxiv2018

An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.

plant biology↗

bioRxiv2017

Trait components of whole plant water use efficiency are defined by unique, environmentally responsive genetic signatures in the model C4 grass Setaria

Plant growth and water use are interrelated processes influenced by the genetic control of both plant morphological and biochemical characteristics. Improving plant water use efficiency (WUE) to sustain growth in different environments is an important breeding objective that can improve crop yields and enhance agricultural sustainability. However, genetic improvements of WUE using traditional methods have proven difficult due to low throughput and environmental heterogeneity encountered in field settings. To overcome these limitations the study presented here utilizes a high-throughput phenotyping platform to quantify plant size and water use of an interspecific Setaria italica x Setaria viridis recombinant inbred line population at daily intervals in both well-watered and water-limited conditions. Our findings indicate that measurements of plant size and water use in this system are strongly correlated; therefore, a linear modeling approach was used to partition this relationship into predicted values of plant size given water use and deviations from this relationship at the genotype level. The resulting traits describing plant size, water use and WUE were all heritable and responsive to soil water availability, allowing for a genetic dissection of the components of plant WUE under different watering treatments. Linkage mapping identified major loci underlying two different pleiotropic components of WUE. This study indicates that alleles controlling WUE derived from both wild and domesticated accessions of the model C4 species Setaria can be utilized to predictably modulate trait values given a specified precipitation regime.

plant biology↗

bioRxiv2017

Raspberry Pi Powered Imaging for Plant Phenotyping

O_LIPremise of the study: Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.\nC_LIO_LIMethods and Results: We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (shape, area, height, color) en masse using open-source image processing software such as PlantCV.\nC_LIO_LIConclusion: This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.\nC_LI

plant biology↗