Plant Methods

Understanding plant growth dynamics requires imaging across day-and-night cycles to quantify growth, movement and development in the aerial plant body and to capture the rhythmic nature of these processes. This requires imaging in light during the day and in darkness at night without perturbing plant physiology. Nighttime imaging has typically depended on infrared (IR) illumination, producing monochrome datasets that require specialised hardware and separate analysis pipelines when combined with daytime RGB imaging. Here, we evaluated very low-intensity green (dimG) illumination from standard LEDs as a practical alternative for colour-consistent nighttime imaging and assessed its physiological impact in Arabidopsis thaliana and Lactuca sativa (lettuce). We show that high resolution colour images can be obtained under dimG using low- cost cameras, with sufficient consistency between full-spectrum and dimG images to allow direct comparison and unified image analysis. We show that very low-fluence green light (<0.5 mol m-2 s-1) does not sustain circadian oscillations of gene activity under continuous exposure and does not perturb rhythms when applied during the dark phase of diel cycles. DimG imaging enabled accurate detection of diel leaf movement profiles in Arabidopsis circadian mutants, revealing genotype-specific phase differences under varying photoperiods. In lettuce, dimG pulses and continuous dimG enabled accurate quantification of diel leaf movement without affecting growth, stomatal opening, electron transport rate or chlorophyll content. Motion profiles under continuous dimG mirrored those under darkness. Our findings establish dim green illumination as a cost-effective solution for night-time imaging, simplifying phenotyping workflows with minimal impact on physiology.

Chlamydomonas reinhardtii is a model green alga extensively used to study photosynthesis and cilia using molecular biology and genetics. Electroporation is a very common technique to transform DNA into the nuclear genome, which is essential to generate mutant collections and express transgenes. Here, we describe a simple, fast, and efficient protocol to transform strains with an intact cell wall. It achieves a good transformation efficiency without cell wall digestion or use of commercial kits and is compatible with the widely available Gene Pulser electroporation system. Key featuresO_LIHigh transformation efficiency of Chlamydomonas reinhardtii strains with an intact cell wall. C_LIO_LIFaster than currently available electroporation protocols. C_LI

Kernel composition traits in maize, including protein accumulation, are of broad interest. The amount of the most abundant proteins in maize endosperm, the -zeins, can vary dramatically among genotypes and in response to soil nitrogen supply. Targeted reductions in -zein accumulation can improve nitrogen utilization and the nutritional quality of maize grain but have traditionally required expensive and destructive phenotyping methods. The Floury2-RFP (Fl2-RFP) reporter gene enables rapid, non-destructive visualization of -zein accumulation in individual maize kernels under white light. This feature is due to the high expression level programmed by the Fl2 promoter, the stability of zein proteins, and the use of monomeric RFP, which emits fluorescence without the need for multimerization. This study aimed to develop a method to quickly document and quantify Fl2-RFP accumulation using camera or smartphone images of either ears or shelled kernels. Results show images of shelled kernels processed with FIJI software capture the Fl2-RFP reporter phenotype better than images of ears. Fl2-RFP confirms the strong maternal control of -zein accumulation and, like grain protein concentration, responds to soil nitrogen supply. The Fl2-RFP phenotyping pipeline effectively quantified Fl2-RFP accumulation by color features from both camera and smartphone images. Smartphone imaging of Fl2-RFP in a diverse population of inbreds followed by elastic net regression of extracted image features predicted kernel protein concentration, as measured by near-infrared spectroscopy, with moderate accuracy (R2 = 0.68, MAE = 0.76, RMSE = 0.93). The spectral features that were most predictive of kernel protein concentration varied depending on whether the background endosperm color was white or yellow. The integrated analysis of Fl2-RFP intensity and grain protein concentration indicates genetic variation for kernel protein accumulation and N-responsiveness that is distinct from the well-studied -zeins. Our findings highlight the Fl2-RFP reporter gene as a valuable tool for investigating the genetic complexity of grain protein concentration and associated traits in maize.

Accurate and reproducible assessment of foliar disease severity is essential for evaluating the performance of heterogeneous plant communities and understanding host-pathogen interactions. However, traditional visual scoring methods remain subjective, with limited precision, and difficult to scale in large phenotyping experiments. Here, we present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously on wheat flag leaves sampled from varietal mixtures. The workflow combines three methodological components: (i) a standardized protocol for leaf sampling and imaging, (ii) supervised machine learning segmentation using Random Forest implemented in Ilastik to classify multiple symptoms (powdery mildew and yellow rust), and (iii) a graphical user interface facilitating pipeline deployment by non-specialist operators. To evaluate the influence of image representation on classification performance, four color spaces (RGB, HSV, HLS, LAB) were systematically compared. The approach was validated using images of durum wheat flag leaves collected from a field experiment assessing eight-way varietal mixtures under natural fungal pressure. Cross-validation against manually annotated images demonstrated high segmentation accuracy across all symptom. Comparison among color spaces revealed only minor differences in performance. Overall, this workflow offers a cost-effective, annotation-efficient and reproducible alternative to deep learning approaches, leveraging open-source and actively maintained tools while requiring limited training data and enabling objective, reproducible and scalable disease phenotyping.

Phenomic prediction (PP) is a breeding value prediction method using near infrared spectroscopy (NIRS). Spectra pre-processing is a key step in the analysis pipeline of PP and generally involves chemometrics methods. However, there is still little understanding in the genetics community of what pre-processing does and why it increases performances. Consequently, the choice of pre-processing is done either arbitrarily or through a search of the optimal set of methods and associated parameters. In this study, we propose a PCA-based pre-processing method where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths. This way, estimations are based on a few informative and orthogonal features of spectra instead of many correlated, uninformative wavelengths. We tested this new pre-processing method on five data sets representing four plant species (maize, rice, sorghum and grapevine). Results show that it performs as good, or better than the best classical chemometric pre-processing methods in almost all cases. Combining PCA-based and classical chemometric pre-processing methods maximizes predictive ability. Moreover, this pre-processing method opens up possibilities of better understanding and selecting parts of the spectral information that are relevant for the prediction of breeding values. Indeed, components representing together about 1% of spectral variability were found to be responsible for most of PP predictive ability. Plain language summaryCultivated plants are the result of a breeding process during which their genetic values are used to select those to breed. Estimation of breeding values requires heavy experimental means and is time consuming. Phenomic prediction is a low cost and high throughput genetic value estimation method that is increasingly being used. It often uses near infrared spectroscopy measurements as predictors of genetic values that are easy to collect and thus routinely used in many species. However, near infrared spectra generally require pre-processing before being used in prediction. Currently used pre-processing methods arise from the chemometrics community, and still deserve a better in-depth appropriation by geneticists. In this study, we propose a new pre-processing approach that performs as good as or better than the best chemometric pre-processing generally used, reduces computation time, and allows for a better understanding of what parts of spectral information are relevant for prediction. Core IdeasO_LIWorking on principal components of spectra instead of wavelengths increases predictive ability of phenomic prediction and performs as good as or better than classical chemometrics pre-processing C_LIO_LIWorking on principal components of spectra requires less optimization of parameters than chemometrics pre-processing C_LIO_LIAbout 1% of spectral variance is responsible for most of the predictive power of phenomic prediction C_LIO_LIWorking on principal components of spectra pre-processed with classical chemometrics pre-processing can increase predictive ability even more C_LIO_LIPCA-based methods are valuable to optimize predictive ability of phenomic prediction and could be used more widely in the quantitative genetics field C_LI

Though currently a minor crop, faba bean is a promising source of plant-based protein as global diets shift towards more plant-based nutrition. To realise this potential, advances in breeding and cultivation are crucial. To exploit heterosis, faba bean breeding frequently utilises synthetic cultivars, which involves open pollination of inbred lines to produce a mixture of F1 hybrid seeds and self-pollinated offspring. Pure F1 hybrid cultivars are currently unavailable due to unstable cytoplasmic male sterility (CMS) systems. An ability to distinguish F1 seeds from their parental inbreds via characteristics associated with xenia effects could change this. The xenia effect refers to the influence of paternal pollen on seed traits, for example seed weight and cotyledon cells in faba bean. In this study, we exploited the xenia effect captured in hyperspectral imaging data to develop machine learning scenarios for discriminating between parental and F1 seeds of open pollinated synthetic combinations (Syn-1). The hyperspectral data were pre-processed using Savitzky-Golay filtering to reduce noise and smooth the spectra. Various machine learning algorithms were applied, incorporating Bayesian hyperparameter optimisation. The scenarios achieved up to 98.9 % accuracy in separating parental components of Syn-1. When including all seeds, the model achieved 40.7 %, indicating moderate detection and classification performance. As the harmonic mean of precision and recall, the F1 score accounts for both the correctness of F1 seed detections and the completeness with which F1 seeds were detected. While this approach does not yet enable the development of full hybrid cultivars, it paves the way for hybrid-enriched cultivars. These could help to streamline breeding for synthetic cultivars and potentially increase yields, for example by increasing the proportion of F1 hybrid seeds in synthetic cultivars. This study extends knowledge of the xenia effect in faba bean and provides a basis for further research aimed at enhancing breeding methods and productivity.

Accurate quantification of leaf lesion severity is essential for plant disease research and phenotyping but is often limited by subjective visual scoring and time-intensive manual image analysis. We present LIME, a fully automated, open-source image analysis pipeline for high-throughput quantification of leaf lesions from disease assay images. LIME integrates zero-shot leaf segmentation using the Segment Anything Model with a convolutional neural network for lesion area estimation. Applied to Arabidopsis thaliana leaves infected with Sclerotinia sclerotiorum, the proposed approach achieved a mean absolute percentage error of 12.9%, comparable to observed intrarater variability in manual scoring. Stratified evaluation across lesion-size groups demonstrated consistent prediction accuracy for small, intermediate, and large lesions, and comparative analysis showed that the deep learning-based model substantially outperformed color-based baseline methods. Under GPU-accelerated execution, LIME processed complete assays containing approximately 200 leaves in 15 minutes, representing an approximate 13-fold reduction in processing time relative to manual annotation. Together, these results indicate that LIME enables objective, reproducible, and scalable quantification of leaf lesion severity in standardized plant pathology assays. The pipeline is released as an open-source tool to support quantitative phenotyping studies.

Functional validation of genetic components in plants often requires cloning them separately into both plant and bacterial expression vectors, a process that is both time-consuming and laborious. This study aimed to simplify this workflow by developing plant-bacteria dual-host promoter systems that drive high-level constitutive expression in both environments. To achieve this, two variants of the chloramphenicol acetyltransferase promoter (PCAT), a bacterial {sigma} factor-dependent promoter, were integrated into the cauliflower mosaic virus 35S promoter (P35S), and their performance was evaluated using a hygromycin phosphotransferase (HPT)-GFP fusion reporter. One of these variants, PCAT1, conferred hygromycin resistance to Escherichia coli (DH5 and BL21 (DE3)) and maintained high-level expression comparable to the original P35S in onion epidermal cells. A hybrid P35S enhancer-PNOS system also conferred hygromycin resistance to E. coli, but its activity in inducing GFP signals in onion cells remained lower than that of P35S. Due to its compact size (89 bp) and efficiency, PCAT1 can serve as a module for converting standard plant vectors into dual-host systems, accelerating gene characterization and the development of new gene-based tools.

Micro-dwarf tomato cultivars are increasingly considered for urban and controlled-environment agriculture due to their compact architecture and suitability for high-density planting. However, optimal canopy management strategies for these cultivars remain poorly defined. In this study, we evaluated the effects of different leaf removal intensities on leaf-level physiological performance, fruit yield, and fruit quality in three micro-dwarf tomato cultivars (Mohammed, Hahms Gelbe Topftomate, and Red Robin) grown under contrasting seasonal light conditions. Plants were subjected to low (15%), moderate (30%), or severe (90%) leaf removal, and leaf-level gas exchange was measured across canopy layers, along with yield and fruit quality assessments. Severe leaf removal (90%) increased carbon assimilation, transpiration, and stomatal conductance in middle and lower canopy leaves by up to approximately twofold compared with control plants, indicating improved light availability at the leaf level. However, these physiological enhancements did not consistently translate into higher yield, reflecting reduced whole-plant source capacity under excessive leaf removal. Low to moderate leaf removal (15-30%) generally increased or maintained yield and fruit number, whereas severe leaf removal reduced yield in Hahms Gelbe and Red Robin, particularly under low seasonal radiation. In contrast, Mohammed exhibited yield increases of up to 220% under low leaf removal and maintained increased yield even under severe leaf removal under high-light conditions. Fruit quality was largely unaffected by leaf removal, except for total soluble solids, which declined by approximately 12% under severe leaf removal across cultivars, consistent with sugar dilution under source limitation. Overall, these results demonstrate that optimal leaf removal in micro-dwarf tomatoes requires balancing improved canopy light distribution with maintenance of sufficient leaf area for carbon assimilation. Leaf removal thresholds are strongly cultivar- and light-dependent, emphasizing the need for cultivar-specific canopy management strategies in compact tomato systems and controlled-environment agriculture.

Seaweed cultivation has recently gained increased attention in North-West Europe as a sustainable source of biomass for biobased products. However, yields need to increase to make the seaweed sector economically viable. To achieve this, higher yielding varieties can be bred but this requires variation for yield and yield-related traits among genotypes. To reliably select high-yielding genotypes, an understanding is required of how both within-farm and between-farm environmental differences affect phenotypes and how to identify simple and reliable proxies for yield. In this study we evaluated growth of nine Saccharina latissima genotypes on two farms, 12 km apart, within the same season. We observed a threefold difference in yield among genotypes, demonstrating the potential for improvement through selection and breeding. Blade thickness and blade size-related traits were strongly correlated with yield, highlighting their potential to serve as rapid and non-destructive proxies for yield, thereby accelerating selection. Furthermore, we demonstrated the importance of adequate replication in farm trials to improve genotype performance estimation by correcting for within-farm spatial variation. Moreover, phenotypic variation was most explained by the genotype and environment, highlighting the importance of both genotype and site selection. Although genotype by environment interactions (GxE) were significant, its contributions were small, indicating stable genotype ranking across farms. Overall, these results are promising for breeding improved S. latissima as it indicates that genotype performance is consistent across close by locations and that local S. latissima populations harbour substantial phenotypic variation that can be used to breed for increased yield. Highlights- Local genetic resources harbour substantial variation in yield and morphology for breeding. - Minor GxE allows for breeding across farms. - Blade thickness and blade size related traits are good predictors of yield. - Correction for on-farm spatial variation improves genotype performance estimation.

Ralstonia pseudosolanacearum (Rps) belongs to the Ralstonia solanacearum species complex (RSSC). It is a vascular pathogen that causes lethal bacterial wilt disease in many plants, including tomato and eggplant. In this study, we infiltrated tomato leaves with the phytopathogenic bacterium at 109 CFU/mL and observed the development of necrotic scars in the infiltrated area at 48 hours post-infiltration. Interestingly, this response was followed by petiole bending toward the ground of the compound leaf. This was followed by the gradual senescence of the infiltrated leaflet only. In addition, the terminal leaflet infiltrated with the pathogen exhibited epinasty. None of the above symptoms were observed in leaves infiltrated with the known virulent deficient hrpB::{Omega} mutant. Surprisingly, all of the above symptoms were observed in leaves infiltrated with another well-known virulence-deficient mutant phcA::{Omega}. It indicated that the necrotic lesion caused in tomato leaves was hrp-dependent. Infiltration in eggplant leaves caused necrotic scarring and leaf senescence, which were relatively delayed. Necrotic scarring without petiole bending or senescence in tomato leaves was also observed due to infiltration of Pseudomonas aeruginosa SPT08, a tomato endophyte having plant growth promotion activity. The patho-phenotypes such as petiole bending, epinasty, and senescence observed in the case of tomato in this study were not reported earlier. We believe these phenotypes produced in tomato after leaf infiltration may be useful to study the virulence of this pathogen.

Citrus yellow vein clearing virus (CYVCV) is the causal agent of an emerging disease representing a potentially high-impact threat for citrus production. Despite remaining outside Europe for decades, CYVCV has now expanded towards two important European citrus producers, Italy and, more recently, Spain. The presence of this virus in the EPPO region represents a current threat with unpredictable and potentially devastating consequences for European citriculture. Therefore, urgent protective measures need to be taken to prevent CYVCV spread and minimize its impact. Diagnostics is a key measure in the management of viral diseases, highlighting the need for harmonized methods suitable for reliable routine detection of the currently known CYVCV diversity. In this study, an inclusive, efficient and highly sensitive real-time RT-qPCR for the detection of CYVCV in plant material and transmission vectors has been developed and validated according to EPPO standards. Moreover, the validated method has been successfully adapted to both PCR digital platforms, that allow high-sensitive absolute quantitative detection, essential in the diagnostics at low viral concentrations; and PCR portable tools, that can be applied in a real diagnostic context for on-site detection. This versatility combines standard validated performance, absolute sensitive quantitation and real on-site detection. The study has also addressed sampling strategies to support reliable molecular diagnostic performance. Our results represent an improvement in the detection of CYVCV to be applied in epidemiological studies and different real diagnostic contexts for the containment of this important citrus pathogen.

In this study, the critical gap in understanding how fonio responds to contrasting pedoclimatic conditions, both within and outside its traditional production areas was addressed. A multi-environment trial was carried out to identify high-yielding genotypes with either broad stability or specific adaptation, thereby enabling targeted varietal recommendations to support the expansion of fonio cultivation into new areas. Randomized complete block design was used in six environments with eleven genotypes to evaluate flowering and maturity times, and grain yield. The Additive Main effect and Multiplicative Interaction and the Genotype main effect and Genotype x Environment interaction biplots revealed a significant effect of the genotype-by-environment interactions on traits, with genotypes B12 and G31 identified as high-yielding, while genotypes M5 and M14 were revealed as early-flowering and maturing. Genotypes M14 and M15 were adapted to all environments and early maturing. Boukoumbe, known as the fonio production area in Benin, was the most desirable for earliness, while Ina was the most ideal for grain yield, proving that fonio could be cultivated in Sudanian and Sudano-Guinean areas. Factor analysis revealed precipitation, C:N ratio, soil pH and texture as the main environmental variables influencing the grain yield in fonio. Our findings contributed to selecting stable, adapted genotypes.

Plant-gall wasp systems provide unique models for studying multitrophic interactions and unique developmental trajectories, yet standardized laboratory protocols for maintaining wild rose hosts (Rosa spp.) and sustaining gall inducers (Diplolepis spp.) are lacking. We developed and tested a method for growing and maintaining translocated individuals of Rosa canina, R. rubiginosa, R. spinosissima, R. gallica, R. tomentosa, and R. pendulina under laboratory conditions over three consecutive years (2023-2026). The goal was to have a constant supply of plant host material for reliably producing galls of D. rosae and D. mayri for experimental use. The protocol integrates soil and substrate composition, photoperiod and humidity regimes, pruning, dormancy management, and controlled exposure to gall-inducing wasps. More than 75% of rose individuals survived the full 3-year period, with consistent annual gall induction across some of the species. This work represents the first reproducible laboratory method for long-term maintenance of wild rose hosts and controlled gall induction by Diplolepis species, while also providing a transferable framework for maintaining perennial woody hosts and experimentally manipulating specialized plant-insect interactions under laboratory conditions, thereby providing a platform for ecological, physiological, and evolutionary studies on these interactions.

Biolistic transformation is a versatile tool in plant science, yet high equipment costs and tissue damage from high-pressure gas remain significant barriers. Building on our previously developed "TSGMAC", a low-cost, helium-free biolistic system, we report three major advancements to enhance its throughput, delivery quality, and quantitative capability. First, a "guide barrel" assembled from commercial DIY fittings was developed; it effectively eliminates physical tissue damage and ensures uniform particle distribution, even in soft tissues like bok choy (Brassica rapa subsp. chinensis). Second, a rapid gene expression platform using PCR products was characterized. Results demonstrate that linear DNA constructs are efficiently circularized via non-homologous end joining (NHEJ) in plant cells, and protein expression is robust regardless of the relative positions of the promoter, coding sequence, and terminator. This system bypasses time-consuming cloning. Third, a cost-effective, highly sensitive dual-luciferase assay system utilizing teal Luc (teLuc) and inexpensive firefly luciferase (FLuc) inhibitors was established. This integrated workflow enables rapid, quantitative molecular biology using supermarket-obtained materials and standard PCR reagents. Our findings provide a practical foundation for plant scientists, synergistically accelerating gene functional analysis and genetic tool development.

Urban environments typically experience higher temperatures than surrounding natural landscapes, making urban vegetation crucial for cooling local areas and improving the health of city residents. Impervious urban surfaces limit the absorption and retention of precipitation, potentially limiting tree water access and threatening long-term survival. Here, we measured tree physiology and growth of Lophostemon confertus (Queensland brush box) trees to investigate how a passive irrigation system that stores stormwater affected the performance of young, establishing trees in a hot and dry suburb of western Sydney, Australia. During the 2024-2025 austral summer, three years after planting, the local climate was periodically hot and dry, with a total of 16 days above 35 {degrees}C. Irrigated L. confertus trees had higher water availability (i.e., higher predawn leaf water potential,{Psi} pre), lower water stress (i.e., higher midday leaf water potential,{Psi} mid, more frequently above turgor loss point), greater stomatal conductance (gs) on hot and dry summer days, and reduced leaf temperatures (Tleaf), compared to control trees. No significant differences in growth rates were observed between irrigated and control trees during the first three establishment years, but irrigated trees had greater crown survival during the hot, dry summer. Our results suggest passive irrigation may mitigate periods of short-term heat and drought stress in urban trees by increasing water access to support transpiration that prevents leaves from overheating, improving tree health. Higher tree transpiration may lead to greater ecosystem services by increasing cooling benefits, contributing to mitigation of urban heat island effects.

In root nodule symbiosis, symbiosome compartments accommodate nitrogen-fixing rhizobia inside the plant cell. Differentiated into bacteroids, the rhizobia are surrounded by a peribacteroid space and a plant-derived peribacteroid membrane, which separates them from the plant cytoplasm but allows signal and nutrient exchange between host and microbe. The morphological features of symbiosomes are primarily determined by ultrastructural single focal plane imaging, with limited information about spatial details. This study combines 2D and 3D imaging, using transmission electron microscopy and focused ion beam scanning electron microscopy as complementary techniques to analyse the symbiosome ultrastructure and organisation in Lotus japonicus wild-type plants. The 3D model of a mature colonised root nodule cell region demonstrates a dense, puzzle-like arrangement of symbiosomes relative to one another and adjacent plant organelles. The symbiosome shape and size depends on the orientation and number of bacteroids within the compartment and features connective tubular structures. Furthermore, vesicular structures, some likely of bacterial origin, were present at the interface. The study presents a multi-angled analysis of symbiosome-related structures, highlighting their volumes, spatial distribution, and pronounced compactness. Interface associated vesicles, protrusions and connective structures hint towards a dynamic and flexible system that contributes to the plant-microbe crosstalk.

Intercropping is a promising strategy to improve productivity and sustainability in agricultural systems, but designing effective genotype combinations remains a major challenge owing to the rapid increase in possible pairings as the number of candidate genotypes increases. This creates a practical bottleneck because field evaluation of all combinations is infeasible under realistic resource constraints. Here, we propose a framework that integrates genomic prediction and Bayesian optimization to support efficient decision-making for intercropping system design. Using genome-wide marker data from sorghum and soybean, we simulated intercropping performance across 5,214 genotype pairs under certain genetic architectures, including variation in heritability, correlations between direct and indirect genetic effects, and the contribution of pair-specific interactions. Genomic prediction models incorporating direct and indirect genetic effects substantially improved prediction accuracy compared with models based on direct genetic effects alone, and inclusion of specific mixing ability further enhanced the performance under high-heritability conditions. When coupled with Bayesian optimization, the models rapidly identified superior genotype pairs, requiring fewer evaluation cycles than random or prediction-only search strategies. Acquisition functions that account for predicted uncertainty were most effective in complex scenarios involving interaction effects or negative correlations between direct and indirect effects. These results demonstrate that combining genomic prediction with Bayesian optimization can substantially reduce the experimental burden associated with intercropping design, while improving the efficiency of identifying high-performing genotype pairs. The proposed framework provides a practical approach for prioritizing candidate mixtures in breeding and field evaluation, and contributes to the development of data-driven strategies for sustainable agricultural systems. HighlightsO_LIA data-driven framework was developed to optimize genotype pairs in intercropping. C_LIO_LIModeling indirect effects improved prediction accuracy across genotype pairs. C_LIO_LIPair-specific interactions enhanced prediction under high-heritability conditions. C_LIO_LIBayesian optimization identified superior pairs under limited evaluation capacity. C_LIO_LIThe framework reduces field-testing requirements for intercropping system design. C_LI

Potato crop is highly vulnerable to abiotic stresses like salinity and low nutrient availability. Rapid identification of stress-resilient genotypes is therefore essential for breeding, yet conventional phenotyping is often slow, space-demanding and expensive. We present LOCOPOTS -- a LOw-COst high-throughput screening platform for in vitro POTatoes under abiotic Stress -- which combines individual in vitro plant culture, low-cost RGB imaging and machine-learning-based automatic segmentation using a trained model of a convolutional neural network, based on U-Net architecture. LOCOPOTS enabled the automated extraction of growth, colour, and vegetation-index traits and demonstrated robust performance across independent phenotyping rounds. We screened 30 potato varieties under control, low-nutrient and saltinity conditions, identifying contrasting growth and physiological responses. Integrated traits such as final area and height, Area_AUC and height_AUC, together with GLI, Chol, cive and chlorophyll fluorescence parameters, discriminated genotype performance under stress. Metabolic profiling further revealed genotype-specific reprogramming in carbon and nitrogen metabolism under low nutrition and salt stress, including changes in fructose, myo-inositol, {beta}-aminobutyric acid, {gamma}-aminobutyric acid, proline, and certain polyamines, identifying them as specific chemical biomarkers of plant stress responses. LOCOPOTS provides a scalable, affordable and space-efficient platform for early screening of potato genetic diversity and identification of candidate traits associated with stress resilience.

The transformation of the free nuclear syncytium into cellular endosperm tissue with starch and protein accumulation is a well-established phenomenon, at least in the fruits of cereals of the Triticeae tribe. The present article demonstrates that there is considerable diversity inherent in this type of caryopsis morphogenesis. By examining various taxa (species, varieties, and cultivars) of wheat, oats, and some wild grasses, this research reveals significant deviations in endosperm morphogenesis from the typical state. A new developmental pattern of endosperm was identified, characterized by several distinctive features such as incomplete cellularization of the syncytium and starch accumulation within the acellular endosperm domains and the endosperm cavity. A large number of plastids were observed in the syncytium stage, which served as the basis for the later amyloplast stage. The acellular endosperm domains and the cavity domain exhibited connections at specific discontinuities in the modified aleurone layer surrounding the cavity. The peripheral parts of the caryopsis received fewer assimilates necessary for starch synthesis, which was attributed to their increased distance from the transfer system and a likely reduction in the efficiency of assimilate transport through the apoplast in these areas. The starch cavity volume constituted a few percent of the overall caryopsis volume, which could serve as a foundation for potential breeding improvements to enhance starch yields across different varieties.