Plant Methods

Non-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function to diagnose factors responsible for reduced growth in commercial and non-commercial settings. In this study, we explored whether the integration of 3D-multispectral (3D) and 2D-hyperspectral imaging (HSI), aided by machine learning (ML), could be used to identify environmental stress treatments imposed during plant growth. Controlled environment-grown Nicotiana Benthamiana plants were subjected to a range of abiotic treatments - including different growth irradiances, heat treatment and drought stress - with the treatments resulting in differences in shoot height, biomass, leaf area and spectral reflectance. ML models were trained to identify these treatments using morphological and spectral traits measured at 27, 29, 31, and 34 days after sowing (DAS). A 3D-multispectral scanner was used to obtain information on plant height, biomass, and leaf area. A visible and near-infrared (VNIR) HSI camera provided detailed spectral information for deriving spectral indices including the Normalised Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI) and Normalized Difference Red Edge (NDRE). Manual measurements provided baseline comparative data. The 3D-multispectral scanner reliably estimated above-ground traits, with high correlations between manual and scanner-derived measurements. The ML models accurately differentiated among environmental stress treatments, with the fused 3D+HSI model achieving the best overall predictive performance across all evaluated metrics compared with models based on either imaging modality alone. Results demonstrated the effectiveness of combining 3D-multispectral and 2D-HSI data with ML analyses for non-destructive, high-throughput phenotyping. The integration of these techniques enabled non-destructive, high-throughput identification of environmental stress treatments imposed during plant growth.

Water-use efficiency (WUE), the ratio of accumulated plant biomass to water lost through transpiration has conventionally been determined using a destructive single-point measurement. Recent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies. Therefore, we compared digital biomass determined point clouds produced from multispectral camera scanners with destructive harvests across eight harvests using Samsun tobacco grown under both drought and high-water conditions. WUE efficiency, calculated using the digital biomass estimated from a point cloud and gravimetric water use determinations, were compared to destructive harvest determinations. The coefficient of variation (CV) showed there were no significant differences in digital and destructive measurements for either biomass or WUE. Indicating that digital measurements can be used in place of destructive measurements. Drought plants used significantly less water and were significantly smaller than high-water plants from Harvests 4 through 8. However, there were no significant differences in the ratio of evapotranspiration to leaf area or WUE, indicating that drought plants were simply smaller and used less water than the high-water plants. This work validates that estimating plant biomass from a digital point coupled with continuous gravimetric determination of water use provides a reliable nondestructive measure of WUE in high-throughput measurements across the full plant life cycle.

Germination percentage is an endpoint measure and therefore does not describe when an individual seed begins visible growth or how rapidly its radicle and plumule expand. We developed a time-resolved phenotyping workflow to quantify rice seed germination continuously in shallow-water culture. A single industrial camera moved along a 1 m rail and imaged three culture boxes at 1 h intervals for up to 80 h. The archive comprised 1,062 full-frame images and 6,372 seed-level repeated observations under the six-seed field-of-view configuration. A physical grid maintained seed identity through time and enabled individual regions of interest to be extracted. Whole-seed foregrounds were obtained with a pretrained U2-Net, and a masked RGB intensity rule separated newly emerging tissue from the darker hull. For each tracked seed, projected emerging-tissue area and interval growth rate were calculated. Three representative normally germinating seeds first showed measurable tissue at 48 h, yet subsequently followed distinct trajectories: final projected areas ranged from 2,605 to 4,700 pixels and peak interval growth rates ranged from 106.88 to 287.92 pixels h-1. B-1 accumulated 63.71% of its final visible area during 72-80 h, whereas B-3 accumulated 73.51% during 60-72 h. Thus, seeds with the same observed emergence interval can differ substantially in the timing and magnitude of post-emergence expansion. The workflow converts repeated images into biologically interpretable temporal phenotypes and provides a basis for nondestructive studies of rice seed vigor and germination heterogeneity.

Developments in Nanopore sequencing have enabled telomere to telomere genomic assembly as a routine technique in genomic research. Nanopore DNA sequencing for genomic assembly is typically performed on native DNA molecules, making it particularly sensitive to the quality of input DNA, with contaminating molecules limiting data yields and reducing read quality. As pangenome analysis gains interest, particularly in non-model plant species which are often rich in inhibitory secondary metabolites, the development of methods which can improve the quality and throughput of nanopore sequencing is essential. Here we describe a method for isolation of total DNA from the leaf tissues of diverse Viridiplantae species. The initial lysis buffer consists of a modified CTAB buffer, incorporating dimethyl sulfoxide for the reduction of viscosity, which can be problematic in many plant DNA preparations. An organic extraction with 2-butoxyethanol is utilized to further extract phenolic compounds which may be sufficiently hydrophilic to evade chloroform extraction, while reducing aqueous phase volume. Further cleanup via cesium chloride (CsCl) ultracentrifugation is performed to minimize the carryover of residual contaminating macromolecules. Samples prepared using this method are of consistent high quality, even when extracted from challenging late season leaf tissue or secondary metabolite rich species. Sequencing results from samples prepared by this method outperform those obtained from typical modified CTAB DNA isolation techniques in both quantity and quality. We tested sequencing performance from Vitis DNA isolated using a modified CTAB method and Vitis DNA isolated using the CsCl ultracentrifugation-based method described here. DNA isolated via the method described here produced 83% more >Q10 sequence data (52.61 Gb vs. 28.8 Gb), resulted in a 60% greater read N50 despite more handling steps (32.78kb vs. 20.45kb), and resulted in a higher modal read quality (Q27 vs. Q24). The consistency of this method across diverse plant taxa suggests its use as a general method for DNA isolation prior to Nanopore sequencing and genomic assembly for diverse plant taxa.

Reliable and objective phenotyping is essential for plant breeding programs to characterize genetic variation and accelerate crop improvement. Conventional disease assessment relies on expert visual scoring, which is labor-intensive, subjective, and prone to inter- and intra-rater variability. Although image-based phenotyping methods have been proposed, many require manual intervention, specialized imaging setups, or single time-point measurements, limiting their ability to capture disease progression over time. Here, we present a pipeline for longitudinal plant disease phenotyping that quantifies wheat stripe rust and leaf rust progression from time-series images. The pipeline performs semi-automated leaf and automated pustule segmentation from images acquired in situ, enabling objective disease severity estimation with minimal user intervention and without requiring solid backgrounds or manual leaf manipulation or detachment. By extracting temporal traits, including disease severity trajectories and standardized area under the disease progress curve, the method provides a comprehensive characterization of disease development throughout infection. Association between automated and expert assessments was moderate for stripe rust (R2 = 0.58) and strong for leaf rust (R2 = 0.85), while expert inter-rater reliability was moderate for both diseases (ICC = 0.675 and 0.800, respectively). The proposed approach establishes a scalable and reproducible framework for longitudinal disease phenotyping in controlled environments, with broad applications in disease resistance screening and crop breeding.

BackgroundThe quantification of fungal spores constitutes a fundamental metric in phytopathology, serving as the primary variable for inoculum standardization and being used as a proxy for disease severity. Historically, spore quantification has relied on manual hemocytometry, which remains the most precise counting process to date, where chambers such as the Malassez slide are used to count a subsample of the inoculum. However, this method applied manually is highly labor-intensive, time-consuming, and can be prone to operator-dependent variability. To overcome these limitations, we introduce MIRA (Microscopy Image Recognition & Analysis), a novel open-source software integrating You Only Look Once (YOLO) deep learning algorithms. Featuring a user-friendly graphical interface, MIRA is adaptable to multiple camera systems and supports advanced object detection models, including YOLOv11 and YOLOv26. ResultsWe demonstrate that MIRA can be used to accurately detect and count spores from several phytopathogenic fungi, automatically measure spore surface area, and to differentiate spores across different genera. In an exhaustive comparative analysis using Pyricularia oryzae spores as an example, MIRA was benchmarked against manual gold-standard counting slides (Malassez and Kova) and indirect spectrophotometric methods (SPARK). The P. oryzae model loaded via MIRA achieved a strong correlation (R = 0.96) with manual gold standards while reducing processing time by over 90% for high-concentration samples (10 spores/mL). Beyond this benchmark, we also successfully tested specific YOLO models designed to recognize macro- and microconidia of Fusarium oxysporum f. sp. cubense, a model for Pseudocercospora fijiensis, and a single multiclass model capable of identifying six different rice pathogenic fungi. We provide comprehensive tutorials for operating the software and training custom detection models for free using Roboflow and Google Colab. MIRA is available both as open-source Python code and as standalone executables for Windows and Linux. ConclusionsMIRA provides a rapid, accurate, and highly reproducible alternative to manual spore counting, effectively removing a major bottleneck in phytopathology workflows. By combining advanced YOLO-based deep learning with an accessible interface and comprehensive training resources, MIRA makes accessible automated image analysis for researchers without programming expertise. Moreover, MIRA drastically improves the efficiency of high-throughput disease phenotyping and can be adapted for a wide range of microscopic quantification tasks across various biological disciplines.

Live imaging of plant subcellular structures is key to deciphering the spatiotemporal bases of cellular processes, and their functional impact on growth and morphogenesis at various biological scales. Live imaging of plant cells essentially relies on expression of fluorescent markers labeling cells or subcellular structures of interest. Simultaneous multi-channel imaging of several markers is still not routine practice in plant cell biology, owing to issues linked to genetic or spectral compatibility of markers, differences in expression levels, silencing, toxicity, etc. Here we designed a three-color marker in Arabidopsis thaliana and Capsella rubella, enabling high-resolution live imaging of plant morphogenesis, including labeling of the cell membrane, the nucleus and the microtubule cytoskeleton. Detection of MT arrays involved the development of a MAP4-MBD-based microtubule marker optimized for plant cells. The three-color marker allows visualization of the three-dimensional organization and dynamics of plant microtubules within the intracellular space with unprecedented precision, in various organs including the root and shoot meristems, the leaf, anther, and gynoecium. Our results demonstrate the potential of such single-construct strategy for cell biology studies in plants.

Multiple vectors and bacterial strains have been developed to enable cloning and amplification of DNA plasmids used in bioengineering applications when transgenic components are toxic to the host. These include plasmids that limit readthrough transcription into transgenic sequences and host strains carrying mutations to minimize recombination or plasmid copy number. However, these techniques are insufficient in cases where transgene expression elements are recognized by the bacterial transcriptional apparatus, or the translation products have functions in cellular metabolism. Here we demonstrate two platforms that mitigate bacterial expression of transgenes driven by the prokaryotic-like promoters of chloroplast transgenes destined for use in plant plastid genetic engineering applications. Both an engineered CRISPRi approach and utilization of the native E. coli Hfq repression system resulted in significant knockdown of plasmid-borne transgene expression, resulting in reproducibly successful cloning and plasmid amplification. The advancements reported here will facilitate synthetic biology studies generally, and enable complex transgenic studies in prokaryotic-like organelles.

High-phenolic plant genera present a major technical limitation in genomic research. Standard extraction approaches that perform reliably across diverse flora often perform poorly when applied to recalcitrant taxa, producing low DNA yield and integrity incompatible with sequencing requirements. The genus Anigozanthos (Kangaroo paws) from the family Haemodoraceae exemplifies this problem. We identified key physicochemical factors governing extraction failure in this genus and resolved them through targeted modifications to lysis chemistry and contaminant management. The resulting protocol achieved a near threefold improvement in DNA purity, substantially reducing contaminant carry over and consistently yielded high-integrity, long DNA fragments (DIN > 7) across a diverse sample set spanning cultivated and wild material across four diverse genera of Haemodoraceae. We also tested a straightforward purity assessment framework that can be implemented in any standard molecular laboratory, enabling rapid pre-submission quality assessment without the need for specialised equipment. Together these advances open a practical path to genomic characterisation of Anigozanthos that establishes a transferable model for genomic research across Australia ' s chemically complex native flora.

Utricularia gibba L. is an aquatic carnivorous plant with a diverse set of capabilities. Reddening of traps frequently occurs in old in vitro cultures. While anthocyanins are often responsible for red coloration in plants, not every plant turns red. Stress factors like high light or excess sucrose have previously been shown to induce the formation of anthocyanins. Here, we hypothesized the red trap formation to be dependent on nutrient deprivation and tested nitrogen deprivation. The results suggest, that only in combination with light, nitrogen deficiency leads to the activation of the complete anthocyanin biosynthesis pathway and visible red coloration. However, in darkness, anthocyanin biosynthesis appears generally less active compared to light conditions and expression of most anthocyanin biosynthesis genes is not significantly upregulated under nitrogen deficiency.

Photosynthesis is among the most consequential yet genetically complex traits in crop plants, and translating its natural variation into actionable genomic targets remains a central challenge for breeding climate-resilient varieties. To start addressing this, researchers are generating increasingly large, multi-environment field photosynthesis datasets. Yet, these data have been structurally under-analysed since their inception. Here we report the outcomes of the first dedicated hackathon focused on computational mining of such field data held in Accra, Ghana, in March 2026. Bringing together data scientists, plant physiologists, geneticists, and breeders from Europe and Africa, these interdisciplinary teams used photosynthetic data collected with hand-held fluorometers to genome-wide marker data across four crop species: cowpea (Vigna unguiculata), barley (Hordeum vulgare), common bean (Phaseolus vulgaris), and potato (Solanum tuberosum). Despite using different species and methods, independent teams identified the same three key findings. First, mechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield. Secondly, machine learning methods proved effective at uncovering genetic associations, with temporally resolved features substantially outperforming single time-point measurements. Third, raw chlorophyll fluorescence and absorbance traces consistently contained more information and predictive power than the extracted parameters currently used. A defining feature of this event was having experimentalists and data scientists working together, enabling AI approaches to be grounded in domain knowledge and biological mechanisms rather than relying on data alone.

Tomato production, yield and fruit quality face major challenges due to several factors, including the complex polygenic inheritance of agronomically relevant traits, biotic and abiotic stresses, and increasingly stringent regulations limiting the use of phytosanitary products. In this context, bioinoculants have emerged as a sustainable strategy capable of enhancing yield without compromising fruit quality, conferring protection against different stresses and exerting a minimal or no impact on environment and human health. In this study, we evaluated the effects and the underlying mechanisms by which Streptomyces sp. N2A, an actinobacteria isolated from soybean rhizosphere, promotes seed germination, vegetative growth and yield in tomato, without modifying fruit quality. The obtained results demonstrated that the bacterial treatment significantly improved seedlin[g]s emergence and growth and development in vegetative stage. At harvest, yield was also significantly enhanced, mainly driven by increased individual fruit weight, which was positively correlated with a thicker pericarp in fruits from N2A-treated plants. Transcriptional analysis during fruit development revealed a coordinated induction of auxin and cytokinin signaling pathways before and after anthesis, providing a hormonal framework that underlies the promotion of pericarp growth. This study provides evidence of the beneficial effect of inoculation with Streptomyces sp. N2A on tomato yield and constitutes the first report describing the modification of fruit morphology and expression of genes involved in phytohormonal modulation during early growth and development, induced by a plant growth-promoting Streptomyces.

Juglans neotropica is an endangered South American walnut species for which standardized descriptions of early development are lacking, limiting its effective use in conservation and restoration programs. We developed a BBCH-scale description of early vegetative growth of J. neotropica based on observations under nursery and field conditions in Colombia. Three principal vegetative stages were described: seed germination (stage 0), leaf development (stage 1), and stem elongation (stage 3). Germination was hypogeal and occurred 30-140 d after sowing, occasionally extending to 180 d. During early growth, leaflet morphology, number, and architecture showed consistent and discrete changes between stages 1 and 3, including shifts in apex, base, margin type, and laminar shape. These modifications indicate that early development is organized into distinct ontogenetic phases rather than continuous variation, marking the transition from juvenile to vegetative adult stages. By providing a standardized, development-based framework independent of chronological age, this BBCH scale facilitates accurate identification and monitoring of seedlings in nursery production, restoration projects, and urban forestry programs. More broadly, this approach contributes to the characterization of ontogenetic phase transitions in tropical tree species and supports the use of development-based criteria for managing early establishment and performance.

The morphology of cyanobacterial colonies plays a key role in harmful cyanobacterial blooms, with implications for their vertical migration, resistance against grazing, and light availability. In this study, we introduce the use of Optical Coherence Tomography (OCT) to investigate the three-dimensional morphology of cyanobacterial colonies. The technique enables non-invasive 3D imaging of colonies up to several millimeters in size, providing access to detailed mesoscale morphological features. Gas vesicles inside cells were shown to strongly improve image quality. We describe the sample preparation and image acquisition protocol, as well as an image processing pipeline that extracts mesoscale morphological features and provides a volumetric visualization of colonies. The method was tested for representative colonies of different cyanobacterial species while a dataset of volumetric images and measured mesoscale features was acquired for natural colonies of Microcystis. We demonstrate the utility of 3D imaging by quantifying the effects of irregular colony morphologies on their flotation velocity and the light availability within colonies. We anticipate OCT to become a key imaging technique to monitor populations of cyanobacterial colonies and investigate colony formation, with potential extensions to other colonial and aggregated organisms in freshwater and marine environments.

Stomata are the pores on plant surface, and these tiny pores are responsible for the flow of gas between plants and atmosphere. Currently, what effects of the broad and continuous increase in stomatal density achieved via genetic engineering on plant growth and development remain poorly understood. The 9 Arabidopsis transgenic lines with increased stomatal density were acquired through overexpressing FSTOMAGEN (the homologs of STOMAGEN, which are in Flaveria). The intermediate stomatal density (SD) lines exhibited increased trend in biomass. Compared with the lines with low SD, the biomass of Arabidopsis lines with intermediate SD (484 mm-2) significantly increased. There was a positive and significant correlation between biomass and relative water content. Across these transgenic lines, only during the earlier phase of growth, the leaf area exhibited a gradually increased trend as stomatal density increased, and there was both a significant linear relationship between SD and leaf growth rate and a strong linear relationship between SD and leaf area. In contrast, a clear relationship during the later phase wasnt observed. Under lower growth light intensity, there was an increased trend of biomass from other lines to the lines with intermediate SD, and the photosynthetic rate and stomatal conductance of the intermediate line were significantly increased. This study reveals plant-growth alterations that correspond to broad and near-continuous increases in stomatal density achieved via genetic engineering. Our study sheds light on the prerequisites for elevated stomatal density achieved via genetic engineering to promote plant growth.

The integration of digital technologies for high-throughput field phenotyping is critical for accelerating crop improvement in agriculture. However, extracting traits from remote sensing data remains constrained by fragmented workflows, manual intervention, and limited interoperability among existing tools, resulting in delays that hinder timely biological insight and decision-making. To address these challenges, we present PhenoStream (Phenotyping Streaming), a scalable, end-to-end cyberinfrastructure designed to automate the full lifecycle of aerial imagery-based phenotyping, from data acquisition to plot- and genotype-level inference. The framework integrates automated data ingestion from distributed field sites, geospatial processing, and AI-enabled trait extraction within a unified, user-accessible graphical interface. Its modular and extensible architecture supports adaptable trait modeling and seamless integration of new data sources, enabling deployment across diverse crops, environments, and experimental designs. We demonstrate the system across a large multi-location field trial network of bioenergy crops, where it enables high-throughput characterization of spatiotemporal growth dynamics, genotype-by-environment (GxE) interactions, and predictive modeling of key agronomic traits. By significantly reducing processing latency and manual effort, the platform facilitates near-real-time analysis and reproducible workflows. This work establishes a generalizable and scalable pathway for operationalizing very-high-spatial resolution aerial phenotyping in agricultural research. By bridging data acquisition and analytics, the end-to-end cyberinfrastructure provides a foundation for integrating heterogeneous and unstructured data streams--including remote sensing, environmental, and management data--toward data-driven decision making in agriculture.

Most transgene applications require high and sustained expression, particularly in stably transformed plants. Achieving optimal transgene performance, however, depends on the combined influence of multiple genetic and regulatory factors. In previous work, we systematically evaluated the contribution of different genetic elements to transient transgene expression and demonstrated that terminators are key determinants of transgene performance by reducing transcriptional read-through and preventing transgene silencing. Here, we extend these findings by investigating the roles of terminators and introns in the expression of transgenes in stably transformed plants. Our results show that optimal transgene performance arises from the complementary actions of these two elements. Terminator choice was a major determinant of transgene expression levels, whereas introns played a critical role in maintaining expression stability. We further demonstrate a strong relationship between transgene expression levels and small RNA accumulation and show that intron-containing endogenous genes are enriched among highly expressed and stress-responsive genes, suggesting that intron-mediated protection from silencing may facilitate higher levels of gene expression and have contributed to the emergence and evolutionary retention of intron-containing genes.

Since the development of CRISPR-based genome editing tools, a number of novel technologies have emerged. This includes Prime-Editing that acts as a search and replace genome editing tool. Prime-Editing has been deployed across multiple clades, including in a few flowering plants. Here, we report on the development of an efficient Prime Editor (PE) for the model bryophyte Marchantia. Initial tests were conducted on Acetolactate Synthase as a target and revealed an average efficiency above 40%. The system has been developed in the GoldenGate cloning system, facilitating construct design. The development of PE in Marchantia expands the Genome-Editing tools available for this emerging model in plant biology.

Red crown rot of soybean (RCR), caused by Calonectria ilicicola, is an emerging soilborne disease whose quantification is challenging due to its complex symptom development across root and foliage levels. This study developed and evaluated a multi-scale framework to improve the assessment of RCR severity from controlled environments to field conditions using root imaging and standardized visual scales. Under controlled conditions, a standard area diagram (SAD) for root necrosis was developed and validated, and SAD-assisted evaluations significantly improved accuracy, precision, and inter-rater agreement compared with unaided assessments. In field conditions, a diagrammatic symptom scale (DSS) was developed using consensus-rated images from experts and showed high reliability, repeatability, and reproducibility across 18 raters, with strong intra- and inter-rater agreement. This study developed and evaluated complementary methods to improve the assessment of RCR severity from controlled environments to field conditions using root imaging and standardized visual scales.

Drought and low temperatures are major abiotic factors affecting key physiological and biochemical processes and limiting the yields of soybean (Glycine max L. Merr.). To in-crease soybean production in Europe, different agricultural strategies are applied to re-duce abiotic stress, including biostimulants. Therefore, studies on the effectiveness of local strains isolated in Europe are becoming increasingly relevant. In this study two bacterial strains Arthrobacter pascens (AP) and Bradyrhizobium japonicum (BJ) along with plant-derived protein hydrolysate (PH) were analysed with soybean plans under abiotic stress conditions in plant growth chambers. Six treatments (control; AP; BJ; PH; BJ+AP; BJ+AP+PH) were tested to evaluate biostimulation effect before stress induction (VC stage) and to determine stress reduction effect on soybeans after plants recovery period (V3 stage). Biostimulants application has positive effect on soyabean biometric parameters in early plant development stage and post stress periods. More stable long-term effect was found on structural plant development parameters, than on pigment accumulation. The best results on plant biometric parameters were found where (AP) and (BJ+AP+PH) com-bination was inoculated. (BJ+AP+PH) combination was the only effective treatment, which showed significantly different results in pigments indices, compared to the control, after stress period. Author summaryYasha Jamil: Conceptualization, Data curation, Formal analysis, Writing- original draft, Giuseppe Colla: Formal analysis, Writing- original draft, Writing- review & editing, Justina Kaziuniene: Data curation, Formal analysis, Sarune Ramoskaite :Writing- review & editing. Monika Toleikien[e]: Conceptualization, Data curation, Formal analysis, Writing- original draft, Funding acquisition, Supervision, Writing- review & editing.