ABSTRACT 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.

Introduction:The phytolith (plant silica) morphotype, Elongate dendritic, is used to indicate the presence of domesticated grasses (cereals) from the Pooideae subfamily, such as wheat and barley, in the archaeological record, but related wild taxa also produce Elongate dendritic that closely resemble those of cereals. By examining the morphometric traits of Elongate dendritic in a diverse set of extant Pooideae taxa, we evaluate its effectiveness as a proxy for cereal domestication and identification. Methods:We investigated the occurrence of Elongate dendritic across a wide range of Pooideae taxa and generated 3D meshes of phytoliths using confocal microscopy. From these meshes, we extracted geometric morphometric and topological traits, which served as input for machine learning (ML) models to assess the taxonomic resolution of Elongate dendritic. Regression models and linear discriminant analyses (LDAs) were applied to test for links between morphometric traits, domestication status, and ploidy level. Results:Our results show that Elongate dendritic occurrence is likely an ancestral trait within Pooideae, with high levels largely confined to Triticeae (wheat, barley, rye) and Avena (oats). Machine learning applied to 3D phytolith traits captured meaningful taxonomic patterns, with more reliable identification at broader taxonomic levels than at finer ones. However, the approach requires further refinement before it can be robustly applied to archaeological samples. Regression models and LDA demonstrated that while domestication significantly influences morphometric variation, ploidy level does not, although further study is warranted. Discussion:These findings offer important guidance for archaeologists and biologists studying crop domestication. By integrating 3D morphometrics, topological data analysis, and ML, this study introduces a new approach to quantitative phytolith identification. Continued expansion of reference datasets, coupled with methodological refinement, will be essential for improving identification at finer taxonomic levels and unlocking the full potential of Elongate dendritic in the study of domestication and 168 cultivation practices.

Fruit quality is crucial in breeding new apple varieties. Before tasting, consumers assess freshness and flavor based on the physical appearance of fruit. Understanding how fruit quality traits such as shape and size vary across diverse apples [ Malus domestica (Suckow) Borkh.] provides a foundation for future breeding efforts. We analyzed images of 5724 apples representing 743 different trees and 534 unique accessions from Canada's Apple Biodiversity Collection to quantify variation in fruit shape and size. To achieve this, we used a pseudo‐landmarking approach paired with traditional linear measurements including length, width, area, solidity, circularity, and aspect ratio. We also incorporated previously collected fruit weight measurements from the same trees. Using a comprehensive measure of shape, we determined that the primary source of variation in apple fruit shape, or morphometric principal component 1 (PC1) which explained 22.7% of the variation, was most highly correlated with the width to length (aspect) ratio of the fruit ( ρ = −0.964, p < 1 × 10 −15 ). In contrast, PC1 was not significantly correlated with differences in fruit size as measured using area and harvest weight. Our findings indicate that two critical aspects of morphological variation in apple—fruit shape and size—are independent, suggesting it is possible to select for a diverse range of fruit shapes while maintaining a consistent and marketable size.

Climate change intensifies biotic and abiotic stresses, threatening global crop productivity. High-throughput phenotyping (HTP) technologies provide a non-destructive approach to monitor plant responses to environmental stresses, offering new opportunities for both crop stress resilience and breeding research. Innovations, such as hyperspectral imaging, unmanned aerial vehicles, and machine learning, enhance our ability to assess plant traits under various environmental stresses, including drought, salinity, extreme temperatures, and pest and disease infestations. These tools facilitate the identification of stress-tolerant genotypes within large segregating populations, improving selection efficiency for breeding programs. HTP can also play a vital role by accelerating genetic gain through precise trait evaluation for hybridization and genetic enhancement. However, challenges such as data standardization, phenotyping data management, high costs of HTP equipment, and the complexity of linking phenotypic observations to genetic improvements limit its broader application. Additionally, environmental variability and genotype-by-environment interactions complicate reliable trait selection. Despite these challenges, advancements in robotics, artificial intelligence, and automation are improving the precision and scalability of phenotypic data analyses. This review critically examines the dual role of HTP in assessment of plant stress tolerance and crop performance, highlighting both its transformative potential and existing limitations. By addressing key challenges and leveraging technological advancements, HTP can significantly enhance genetic research, including trait discovery, parental selection, and hybridization scheme optimization. While current methodologies still face constraints in fully translating phenotypic insights into practical breeding applications, continuous innovation in high-throughput precision phenotyping holds promise for revolutionizing crop resilience and ensuring sustainable agricultural production in a changing climate.

Many microbes provide benefits to their plant hosts. Over 100 microbial-based products are available to farmers, but their performance is highly variable. Little is known about the factors that influence the colonization and persistence of microbes across the entire root system architecture, despite the importance of these processes to the efficacy of microbial-based products. We investigated whether large mesocosms could be used to study the 3D spatial patterning of microbes and correlated metabolites across mature corn root systems. The experimental design consisted of 12 mesocosms structures (1 x 1 x 1.3 m), half of which were treated with a synthetic community (SYNCOM) of 18 different bacterial isolates. Although none of the 18 members of the SYNCOM was detected at the end of the experiment, treatment with the SYNCOM caused a long-term shift in the microbiome, specifically at the zones furthest from the original seed. Comparison of replicate samples within and between boxes revealed that most of the highly abundant bacteria were observed by sampling as few as three replicates. However, each box and replicate sample also contained at least a few unique bacteria. The root metabolome was much more consistent across space and unaffected by the SYNCOM treatment; however, several individual metabolites were identified that varied based on depth within the boxes. This work establishes the mesocosm system as a viable experimental platform for tracking microbe-metabolite correlations across a root system and confirms the hypothesis that significant variability exists in the root-associated microbiome across a full-grown root system.Copyright (c) 2025 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

The rapid increase in atmospheric CO2 levels has profound effects on plant systems, making it essential to understand how plants develop under elevated CO2. By perturbing epigenetic mechanisms necessary for plant acclimatization, we conducted in-depth analyses of the Arabidopsis thaliana 3D genome to investigate the relationship between epigenetic memory and 3D chromatin architecture. Using methylation-sensitive chromatin conformation capture (Hi-C), we reveal how elevated CO2 induces chromatin decondensation resulting from changes in 5mC levels and histone modifications at developmentally regulated loci. Utilizing integrated deep sequencing, we uncover local domains of chromatin loops in the Arabidopsis genome that orient clusters of developmentally regulated loci, revealing a topological framework for stress response pathways. At high resolution, our analyses uncover differential chromatin loops supported by transcriptionally reprogrammed anchor loci, revealing a collaboration between RNA-directed DNA Methylation (RdDM) and Polycomb group (PcG) proteins in mediating gene expression regulation. Our findings demonstrate the central role of the 3D genome and epigenetic modifications in plant development, adaptation, and resilience in changing environments. Finally, we report a mechanism by which elevated CO2 initiates epigenetic reprogramming underlying an accelerated growth phenotype. ### Competing Interest Statement The authors have declared no competing interest. U.S. National Science Foundation, https://ror.org/021nxhr62, EF-1921724

A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.

ABSTRACT Background Recent developments in hybridization chain reaction (HCR) have enabled robust simultaneous localization of multiple mRNA transcripts using fluorescence in situ hybridization (FISH). Once multiple split initiator oligonucleotide probes bind their target mRNA, HCR uses DNA base-pairing of fluorophore-labeled hairpin sets to self-assemble into large polymers, amplifying the fluorescence signal and reducing non-specific background. Few studies have applied HCR in plants, despite its demonstrated utility in whole mount animal tissues and cell culture. Our aim was to optimize this technique for sectioned plant tissues embedded with paraffin and methacrylate resins, and to test its utility in combination with immunolocalization and subsequent correlation with cell ultrastructure using scanning electron microscopy. Results Application of HCR to 10 µm paraffin sections of 17-day-old Setaria viridis (green millet) inflorescences using confocal microscopy revealed that the transcripts of the transcription factor KNOTTED 1 ( KN1 ) were localized to developing floret meristem and vascular tissue while SHATTERING 1 ( SH1 ) and MYB26 transcripts were co-localized to the breakpoint below the floral structures (the abscission zone). We also used methacrylate de-embedment with 1.5 µm and 0.5 µm sections of 3-day-old Arabidopsis thaliana seedlings to show tissue specific CHLOROPHYLL BINDING FACTOR a/b ( CAB1 ) mRNA highly expressed in photosynthetic tissues and ELONGATION FACTOR 1 ALPHA ( EF1 α ) highly expressed in meristematic tissues of the shoot apex. The housekeeping gene ACTIN7 ( ACT7 ) mRNA was more uniformly distributed with reduced signals using lattice structured-illumination microscopy. HCR using 1.5 µm methacrylate sections was followed by backscattered imaging and scanning electron microscopy thus demonstrating the feasibility of correlating fluorescent localization with ultrastructure. Conclusion HCR was successfully adapted for use with both paraffin and methacrylate de-embedment on diverse plant tissues in two model organisms, allowing for concurrent cellular and subcellular localization of multiple mRNAs, antibodies and other affinity probe classes. The mild hybridization conditions used in HCR made it highly amenable to observe immunofluorescence in the same section. De-embedded semi-thin methacrylate sections with HCR were compatible with correlative electron microscopy approaches. Our protocol provides numerous practical tips for successful HCR and affinity probe labeling in electron microscopy-compatible, sectioned plant material.

The grasses are one of the most diverse plant families on Earth, however, their classification and evolutionary history are obscured by their pollen stenopalynous (similar) morphology. A combination of high-resolution imaging of pollen surface ornamentation and computational analysis has previously been proposed as promising tool to classify grass taxonomic boundaries. In this study, we test this hypothesis by studying Poaceae pollen across the phylogeny from plants collected in northern South America, but also from published literature across the globe. We assessed if morphotypes that we establish using descriptive terminology are supported by computational analysis, if they vary along six (a)biotic variables and how vary across the phylogeny. Based on this analysis, we constructed a reference framework for pollen surface ornamentation morphotypes. Our results showed that there is a very wide variation of grass pollen surface ornamentation. We identified nine new and six known morphotypes and established our dataset of 223 species (243 individual plant specimens) from 11 subfamilies. Computational analysis showed that our morphotypes are well-supported by two quantitative features of pollen sculptural elements (size and density). The specific dataset and mapping of the phylogeny confirmed that pollen morphological sculpture is unrelated to (a)biotic variables but is diverse across through the phylogeny.

Grass-dominated biomes in South America comprise c. 20 million years of history, yet their evolution and underlying drivers remain poorly understood. Here we apply a novel approach that combines scanning electron microscopy imaging with computational analysis to quantify the morphometrics of grass (Poaceae) pollen micro-ornamentation from the Neotropics since the Early Miocene (23 million years ago). Three spatial-temporal pollen sets were assembled to further elucidate the variation and evolutionary traits of grasses through space and time. Our results reveals that three spatial-temporal pollen groups occupy unique, partially overlapping regions of their exine morphospace. The direction of this shift is consistent over time, progressing towards less dense ornamentation. Interestingly, the extent of the occupied morphospace did not vary significantly. This is the first time that the true morphological variation in Poaceae pollen micro-ornamentation becomes apparent through time. We hypothesize that changes in grass pollen exine since the Early Miocene were driven by evolutionary processes (evolutionary drift and/or directional selection), and potentially migration at the continental scale. The high diversity in pollen micro-ornamentation is likely related to their evolutionary success in the Neogene.

Common bean ( Phaseolus vulgaris ) is an essential crop. Unfortunately, it is hypersensitive to abiotic stresses, especially drought. Recently, chemical applications have proved to be a prominent approach for enhancing plant abiotic stress tolerance. Although acetic acid application has been reported to rescue plants from abiotic stresses, the function of acetic acid in drought responses in the common bean remains unclear. In this study, we investigated the effects of acetic acid on enhancing drought tolerance of common bean and their underlying phenotypic responses, and physiological and biochemical mechanisms. Our results showed a higher survival rate in acetic acid-pretreated than water-pretreated plants after the recovery from drought treatment. Acetic acid-pretreated plants exhibited greater chlorophyll, carotenoid and anthocyanin contents, higher relative water content, and a lower rate of electrolyte leakage compared with water-pretreated plants under drought. In comparison with water-pretreated plants, acetic acid-pretreated plants maintained higher photosynthetic performance as indicated by higher gas exchange and chlorophyll fluorescence parameters in responses to drought. A higher antioxidant capacity was shown in acetic acid-pretreated than water-pretreated plants, which helped plants detoxify drought-induced reactive oxygen species (ROS), thereby providing better adaptation to drought. Our results indicated that acetic acid pretreatment enhanced drought tolerance in common bean plants by increasing the photosynthetic performance and maintaining ROS homeostasis. This study will provide an avenue for agricultural applications using acetic acid pretreatment to mitigate drought stress in common bean and other crops.

Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft(3) (1.27 m(3)), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new "3D Root Mesocosms" and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.

Heterotrimeric G-proteins modulate multiple signaling pathways in many eukaryotes. In plants, G-proteins have been characterized primarily from a few model angiosperms and a moss. Even within this small group, they seem to affect plant phenotypes differently: G-proteins are essential for survival in monocots, needed for adaptation but are nonessential in eudicots, and are required for life cycle completion and transition from the gametophytic to sporophytic phase in the moss Physcomitrium (Physcomitrella) patens. The classic G-protein heterotrimer consists of three subunits: one Gα, one Gβ and one Gγ. The Gα protein is a catalytically active GTPase and, in its active conformation, interacts with downstream effectors to transduce signals. Gα proteins across the plant evolutionary lineage show a high degree of sequence conservation. To explore the extent to which this sequence conservation translates to their function, we complemented the well-characterized Arabidopsis Gα protein mutant, gpa1, with Gα proteins from different plant lineages and with the yeast Gpa1 and evaluated the transgenic plants for different phenotypes controlled by AtGPA1. Our results show that the Gα protein from a eudicot or a monocot, represented by Arabidopsis and Brachypodium, respectively, can fully complement all gpa1 phenotypes. However, the basal plant Gα failed to complement the developmental phenotypes exhibited by gpa1 mutants, although the phenotypes that are exhibited in response to various exogenous signals were partially or fully complemented by all Gα proteins. Our results offer a unique perspective on the evolutionarily conserved functions of G-proteins in plants.

Color patterning contributes to important plant traits that influence ecological interactions, horticultural breeding, and agricultural performance. High-throughput phenotyping of color is valuable for understanding plant biology and selecting for traits related to color during plant breeding. Here we present ColourQuant, an automated high-throughput pipeline that allows users to extract color phenotypes from images. This pipeline includes methods for color phenotyping using mean pixel values, a Gaussian density estimator of CIELAB color, and the analysis of shape-independent color patterning by circular deformation.

Edamame is a type of green, vegetable soybean and improving shoot architecture traits for edamame is important for breeding of high-yield varieties by decreasing potential loss due to harvesting. In this study, we use digital imaging technology and computer vision algorithms to characterize major traits of shoot architecture for edamame. Using a population of edamame PIs, we seek to identify underlying genetic control of different shoot architecture traits. We found significant variations in the shoot architecture of the edamame lines including long-skinny and candle stick-like structures. To quantify the similarity and differences of branching patterns between these edamame varieties, we applied a topological measurement called persistent homology. Persistent homology uses algebraic geometry algorithms to measure the structural similarities between complex shapes. We found intriguing relationships between the topological features of branching networks and pod numbers in our plant population, suggesting combination of multiple topological features contribute to the overall pod numbers on a plant. We also identified potential candidate genes including a lateral organ boundary gene family protein and a MADS-box gene that are associated with the pod numbers. This research provides insight into the genetic regulation of shoot architecture traits and can be used to further develop edamame varieties that are better adapted to mechanical harvesting.

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. 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 range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image. 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.

Coleus (Coleus scutellarioides) is a popular ornamental plant that exhibits a diverse array of foliar color patterns. New cultivars are currently hand selected by both amateur and experienced plant breeders. In this study, we reimagine breeding for color patterning using a quantitative color analysis framework. Despite impressive advances in high-throughput data collection and processing, complex color patterns remain challenging to extract from image datasets. Using a phenotyping approach called "ColourQuant," we extract and analyze pigmentation patterns from one of the largest coleus breeding populations in the world. Working with this massive dataset, we can analyze quantitative relationships between maternal plants and their progeny, identify features that underlie breeder-selections, and collect and compare public input on trait preferences. This study is one of the most comprehensive explorations into complex color patterning in plant biology and provides insights and tools for exploring the color pallet of the plant kingdom.

The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.