Plant Methods

Potato common scab (Streptomyces sp.) is an economically important disease that reduces the quality and market value of tubers. A key aspect in developing management strategies involves accurately quantifying the disease. Due to the three-dimensional nature of the tuber and the heterogeneous distribution of lesions across its surface, visual estimates of severity can be challenging. Therefore, the objectives of this study were to develop and validate a standard area diagram (SAD) for estimating common scab severity on potato tubers and to compare validation outcomes obtained using real tubers and digital images. A SAD comprising six severity levels (from 1.3 to 66.8%) was developed based on image analysis of naturally infected tubers. Validation was conducted using two complementary approaches in which inexperienced raters evaluated either real potato tubers or digital images of the same tubers under unaided and aided conditions. Accuracy, bias components, and inter-rater reliability were quantified using absolute error metrics, Lins concordance correlation coefficient, intraclass correlation coefficients, and overall concordance correlation coefficients. Use of the SAD significantly improved accuracy, reduced systematic bias, and increased inter-rater reliability across both validation approaches. No significant differences were detected between assessments conducted on real tubers and images, although image-based evaluations showed a slight, non-significant tendency toward reduced scale and location bias under aided conditions. These results demonstrate that a dimension-aware SAD integrating information across the full tuber surface enhances the reliability and reproducibility of visual severity assessments and supports the use of image-based evaluations for training, large-scale surveys, and remote or collaborative applications involving three-dimensional plant organs.

The nucleus is the characteristic organelle for eukaryotic organisms. Unlike the classic textbook view of static two-dimensional nuclei, nuclear shape is dynamic inside the live cell. The alteration or deformed nuclear shape is the hallmark of cancer in animal cells and environmental stress in plants. The nuclear envelope proteins interact with chromatin to regulate gene expression. Unfortunately, we have limited knowledge about the impact of abiotic stress on nuclear shape, movement, and chromatin dynamics. To circumvent this issue, we are utilizing a dual fluorescently tagged marker lines - nuclear envelope protein and chromatin - to perform live cell imaging in the model plant Arabidopsis thaliana root. The live cell imaging was performed in control and salt-stressed conditions. We utilized these captured movies to analyze through open-source image processing software Fiji/ImageJ with the help of the TrackMate plugin. Using this method, we have demonstrated that chromatin velocity is decreased in salt-treated conditions. This method will be widely applied to quantitative live cell imaging of nuclear shape and chromatin dynamics during plant development and environmental stress. SummaryThis process aims to simultaneously record nucleus and chromatin dynamics in Arabidopsis thaliana roots and investigate changes in these dynamics in response to developmental and environmental cues.

Genetically encoded biosensors are one of essential tools in biological research. They enable visualization of molecules of interest from the subcellular level to entire organism level in vivo and can be used to monitor presence of small molecules, gene expression, protein activity, and protein degradation. However, multiplexing fluorescent biosensors in plants is notoriously difficult due to signal bleed-through and strong autofluorescence from chlorophyll. In this study, we investigated the potential of multiplexing biosensors based on the selection of reporter fluorescent proteins. We characterized the emission spectra, fluorescence lifetimes, and relative brightness of diverse fluorescent proteins in plant leaves. We show that selected proteins exhibit comparable brightness, supporting their use in co-expression experiments and reliable quantification of individual signals. To separate overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing. We identified channel separation unmixing approach as the most suitable for biosensors. Additionally, we show how unmixing with the selected approach can be applied to separate autofluorescence and we validated this approach in virus-infected cells by following organelle dynamics in vivo. Overall, our work demonstrates that biosensors can be multiplexed, even when their emission spectra overlap. Significance statementMultiplexing genetically encoded biosensors in plants has been limited by overlapping fluorescent signals and strong autofluorescence. This study presents an optimized framework for linear unmixing and provides a MATLAB-based organelle segmentation tool, allowing precise quantification of multiple fluorescent reporters in vivo and advancing real-time visualization of complex cellular processes in plants.

Fruit size and shape, which influence horticultural quality, are determined by the number and the size of the cells in the local region. In fruit trees, however, the difficulty of applying molecular genetic approaches has hindered a detailed understanding of the localization and orientation of cell division in developing fruit tissues. In this study, we established a novel framework to visualize cell division in pre-anthesis ovaries of three drupe crops, peach (Prunus persica), Japanese apricot (P. mume) and the interspecific hybrid Japanese apricot (P. salicina x P. mume), providing clear insight into the spatial distribution and orientation of dividing cells. We systematically optimized a 5-ethynyl-2'-deoxyuridine (EdU) labeling protocol for thick ovary tissues by adjusting infiltration conditions and fixation methods. In addition, electron microscopy combined with wide-view tiling visualization was applied to directly identify dividing cells, including those undergoing chromosome segregation and cell plate formation. By combining with machine learning-based detection, we efficiently and objectively identified dividing cells. Using these complementary approaches, we found that cell division activity was broadly distributed throughout pre-anthesis ovaries in all three crops, without pronounced spatial restriction. In contrast, analysis of division orientation revealed region-specific patterns: cells in the outermost exocarp divided predominantly anticlinally, whereas cells in the mesocarp divided largely periclinally, consistent with subsequent ovary (fruit) enlargement. The integrated framework presented here provides a foundation for understanding the spatial and three-dimensional regulation of fruit development and for future studies in fruit morphogenesis and horticulture.

Grapevine Trunk diseases (GTDs) represent a major threat for the wine industry. Despite several break-through, their etiology remains unclear and no curative treatment is currently available. Wood anatomy and water transport contribute to the symptoms of young plant decline. This study investigates wood anatomical alterations in two Alsatian grapevine cultivars presenting different susceptibility to GTDs, focusing on wood structure over six months of vegetative growth and in response to infection. Using a validated FasGa staining protocol, wood sections from transverse, tangential, and radial directions were stained to differentiate lignified and cellulosic tissues. Microscopic analysis was performed at x4, x10, and x40 magnifications, yielding a dataset of 4771 images. To support this high-throughput quantitative analysis of microscopy images, a computational model was developed, enabling reliable and efficient assessment of anatomical traits. Pre-established woody tissues presented higher xylem vessels diameter in Gewurztraminer than Riesling, with a dorsoventral arrangement whereas the number of vessels remained the same all over the cross section. No significant anatomical changes were observed in established woody tissues, whereas newly formed xylem anatomy showed a possible rearrangement during infection, especially in Gewurztraminer cultivar. Furthermore, colorimetric analysis quantified the lignification of woody tissues in response to wounding damage compared to un-treated plants. While definitive conclusions remain limited due to the experimental timeframe and sample variability, the findings highlight the need for longer-term studies and broader cultivar evaluation. Code and microscopy images have been made publicly available, providing a scalable digital tool for future research in plant vascular systems.

Lodging in sorghum presents a significant challenge for plant breeders due to the trade-off between lodging resistance and grain yield. Manually measuring lodging across thousands of plots is time-consuming, expensive, and error-prone, making selection for lodging resistance challenging in breeding programs. Unmanned Aerial Vehicle (UAV) derived metrics offer a potential high-throughput, cost-effective alternative for lodging phenotyping. This study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots. Multi-temporal canopy height data were collected at two critical time points: maximum crop height and at manual lodging assessment. Height percentiles were extracted from UAV derived point clouds generated using photogrammetric algorithms. These data were used to develop parametric, non-parametric, and ensemble prediction models, which were evaluated using three statistical metrics. The ensemble model, averaging predictions from all models, achieved the highest accuracy with Pearson correlations of r = 0.80-0.84 and lowest residual mean square error (RMSE=16-18), explaining 64-70% of variation in manual lodging counts. Model diagnostics and iterative refinement, including inspection of UAV imagery and dataset curation, had minimal impact on model performance, demonstrating the robustness of the approach. Model performance was consistent across sites, with minimal effects of stratified sampling on accuracy, confirming the ensemble approach as optimal for plot-level lodging assessment. This study demonstrates that integrated multi-temporal UAV imagery offers a practical alternative to labor-intensive manual evaluation methods by enabling high-throughput lodging assessment suitable for implementation in sorghum breeding programs.

Quantification of root anatomical traits such as cortical aerenchyma is key to understanding rice adaptation to diverse water regimes. Recently, the role of aerenchyma in regulating methane emissions has been demonstrated, making it a target for climate change mitigation. Despite its importance, breeding for root anatomical traits remains limited because manual analysis of root cross-sections is labor-intensive, inconsistent, and poorly scalable, and analysis pipelines do not generalize across heterogeneous imaging conditions. We present a deep learning pipeline based on a recent vision transformer architecture to automatically segment rice root anatomical structures and quantify aerenchyma. The model was trained on a multi-environment dataset of 1,760 annotated rice root cross-sections acquired across growth stages, cultivation systems, and countries, using a collaboratively defined annotation protocol. The model achieved high segmentation performance (mean Intersection-over-Union > 0.92) and near-perfect aerenchyma ratio quantification (R2 = 0.98), and was evaluated by two experts as performing on par with, and in some cases better than, expert annotators. Delivered as open-source software with an online interactive demonstrator, the pipeline revealed differences in aerenchyma across genotypes, water regimes, environments, and developmental stages. Overall, this work demonstrates that transformer-based segmentation enables high-throughput anatomical phenotyping, supporting scalable and climate-smart rice breeding. HIGHLIGHTSO_LITransformer-based segmentation enables robust aerenchyma phenotyping across environments C_LIO_LIA SegFormer model achieves expert-level accuracy on diverse rice root cross-sections C_LIO_LIAutomated analysis delivers near-perfect lacuna-to-cortex ratio quantification (R2 {approx} 0.98) C_LIO_LIOur online demonstrator supports scalable, climate-smart rice breeding applications C_LI

Long-read sequencing has shown a rapid technological development during the last years. It has been established as the standard method for the sequencing of plant genomes and has also gained importance for full plasmid sequencing. As Sanger sequencing has a limited read length of about 1 kb, long read sequencing offers a great advantage, as the full plasmid can be sequenced in one read. Here, we present a cost-effective workflow to sequence full plasmids and compare the results against an expectation. The per plasmid cost of this workflow is determined by the number of plasmids investigated simultaneously, but can be lower than the price of a single Sanger sequencing reaction. We developed a workflow for automatic data processing, which allows us to complete sequencing and data analysis within a day.

Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining solar energy capture via photovoltaic panels with crop production. However, in-depth information on plant growth patterns within the spatially heterogenous microclimate created by the intermittent shading of APVs is largely missing. In the present study, we implement a customized robot-mounted 3D-multispectral imaging system to closely monitor the growth and spectral reflectance patterns of a conventional soybean cultivar "Eiko" (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in plant height, 3D leaf area, light penetration, and canopy volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would enable better crop management within such non-homogenous cultivation systems.

Field-based monitoring of tree species in forests is often sparse due to logistical constraints. Remote sensing enables repeated, spatially contiguous collection of reflectance data across large areas. Tree species classification accuracy using such data is variable, likely because most studies use observational datasets where species occurrence correlates with environmental variation. We used two sites of a tree biodiversity experiment in Germany (BIOTREE: Kaltenborn and Bechstedt), where different species have been planted with high replication under controlled diversity levels, to assess how well tree species could be classified using reflectance data from airborne imaging spectroscopy and different classification methods (linear discriminant analysis, LDA, and a non-linear support vector machine, SVM). Reflectance data for 589 wavelengths between 400-2400 nm were acquired at 1 m spatial resolution during peak growing season. Reflectance spectra showed large and significant variation between taxonomic classes, orders, and species, and weak, but still significant, interactions between classes or orders and diversity levels. Classification accuracy reached 100% in training datasets, 77%-83% for the four species in Kaltenborn prediction datasets, and 31%-49% for the 16 species in Bechstedt prediction datasets. LDA provided more accurate predictions than SVM; and using similarly-spaced original wavelengths with LDA was as efficient as using principal components derived from the original data. While airborne imaging spectroscopy effectively distinguished up to four tree species in our datasets, classification accuracy was lower in more species-rich plots. In these cases, the methodology may be more useful for functional diversity monitoring than for tree species classification.

Crop root systems develop in biologically complex soils where beneficial symbionts and pathogenic organisms can jointly influence root architecture and, consequently, belowground function. In this work, we used X-ray computed tomography (CT) to assess how colonisation by the arbuscular mycorrhizal fungus Rhizophagus irregularis (AMF) and infection by the potato cyst nematode Globodera pallida (PCN) influence root system architecture in soil-grown tomato and potato plants. Root architectural traits, including root volume and root surface area, were quantified non-destructively from intact root systems to evaluate the individual and combined effects of AMF colonisation and PCN infection over time. AMF inoculation increased root volume and surface area, whereas PCN infection caused pronounced reductions in these traits, particularly during early development. AMF-associated increases in root system size were maintained in both PCN-free and PCN-infected plants, indicating largely additive effects of beneficial and pathogenic soil biota on root architectural outcomes. These findings show that soil organisms can independently reshape crop root development in ways likely to influence soil exploration and resource acquisition under biologically complex conditions. More broadly, the study highlights the value of X-ray CT as a non-destructive approach for linking belowground biotic interactions with functionally relevant root traits in sustainable agroecosystems.

Live imaging is one of the most powerful methods to reveal the morphogenesis of plant organs. However, the highly three-dimensional structure of plant organs always poses technical challenges. For example, the basal region of leaf primordia is rarely observed because of the shape of leaf primordia and the sudden shift in geometry at the point where the leaf primordium connects to the hypocotyl. In this work, we developed a new live-imaging system that is suitable for observing the developmental process of the basal region of Arabidopsis leaf primordia at early stages. Using this system, we achieved continuous observation of the basal region of early Arabidopsis leaf primordia for more than 50 hours.

1Accurate quantification of plant disease is essential for resistance breeding, variety testing, and precision agriculture, yet visual ratings are limited by subjectivity, low precision, and restricted throughput. Image-based phenotyping can address these limitations, but field applications face substantial challenges due to spatial heterogeneity, symptom-level diagnostic requirements, and the need for very high-resolution imagery with limited spatial coverage. This introduces a fundamental trade-off: high-resolution images provide precise local measurements of disease, but spot-level estimates can be highly variable within experimental units. We analyzed a large image data set of wheat foliar diseases to characterize the distribution, spatial dependence, and aggregation behavior of spot-level severity estimates in plots. We combined high-resolution macro-scale imaging with focus bracketing to increase the sampled leaf area. Our results highlight focus bracketing as a promising approach for simultaneous diagnosis and quantification of disease in field plots. Autocorrelation in severity estimates both within focal image stacks and across plot positions was comparable, with 10 focal stack images or 10 positions per plot contributing approximately 2.5 independent observations each. Modeling plot-level severity as a latent Beta-distributed variable enabled robust estimation of mean severity and associated uncertainty. This supports both hypothesis testing and efficient sampling across the full range of disease severity associated with genotypic diversity and seasonality of developing epidemics. The proposed imaging approach is non-invasive and, in principle, transferrable to autonomous ground-based phenotyping platforms, offering the potential to shift the dominant source of uncertainty in estimating disease severity from measurement-related limitations toward biologically and environmentally driven variability in disease expression.

Fluorescent in situ hybridization (FISH) enables highly sensitive, high-resolution detection of gene transcripts. Moreover, by employing multiple probes, this technique allows for multiplexed, simultaneous detection of distinct gene expression patterns spatiotemporally, making it a valuable spatial transcriptomics approach. Owing to these advantages, FISH techniques are rapidly being adopted across diverse areas of basic biology. However, conventional protocols often rely on volatile, toxic reagents such as formalin or methanol, posing potential health risks to researchers. Here, we present a safer protocol that replaces these chemicals with low-toxicity alternatives, without compromising the high detection sensitivity of FISH. We validated this protocol using both in situ hybridization chain reaction (HCR) and signal amplification by exchange reaction (SABER)-FISH in frozen sections of various model organisms, including mouse (Mus musculus), amphibians (Xenopus laevis and Pleurodeles waltl), and medaka (Oryzias latipes). Our results demonstrate successful multiplexed detection of morphogenetic and cell-type marker genes in these model animals using this safer protocol. The protocol has the additional advantage of requiring no proteolytic enzyme treatment, thus preserving tissue integrity. Furthermore, we show that this protocol is fully compatible with EGFP immunostaining, allowing for the simultaneous detection of mRNAs and reporter proteins in transgenic animals. This protocol retains the benefits of highly sensitive, multiplexed, and multimodal detection afforded by integrating in situ HCR and SABER-FISH with immunohistochemistry, while providing a safer option for researchers, thereby offering a valuable tool for basic biology.

Potato (Solanum tuberosum L.) is a staple crop crucial to global food security, yet its production is severely threatened by late blight (LB), caused by Phytophthora infestans, one of the most destructive plant diseases worldwide. Breeding programs for LB resistance have traditionally relied on labor-intensive and subjective visual assessments, which limit scalability and consistency, particularly in early-generation trials. Unmanned aerial vehicle (UAV)-based remote sensing combined with machine learning (ML) offers a promising alternative for objective, high-throughput disease phenotyping. This study evaluated the potential of UAV-derived multispectral imagery and ML techniques to estimate LB severity across large and genetically diverse potato breeding populations, comprising 2,745 clones in one trial and 492 accessions in another, conducted in Oxapampa, Pasco, Peru. We compared vegetation index-based approaches with a machine learning framework that integrates K-means clustering and Kernel Ridge Regression (KRR) and assessed their ability to capture genotypic variation and support selection decisions. NDVI consistently showed a strong correlation with visually assessed LB severity, particularly at advanced stages of disease development, enabling objective discrimination between healthy and diseased canopy tissues. However, the KRR-based approach outperformed linear NDVI-based models by capturing nonlinear relationships between spectral responses and disease progression. Estimates of LB severity derived from NDVI and KRR models, expressed as best linear unbiased estimates (BLUEs), showed strong and biologically consistent relationships with the area under the disease progress curve (AUDPC), particularly during later UAV acquisitions. Selection coincidence between UAV-derived estimates and AUDPC-based rankings was substantially higher at intermediate to advanced stages of disease progression, suggesting that UAV assessments at these stages may capture sufficient phenotypic variation to distinguish genotypes. These findings indicate that UAV-based multispectral phenotyping, especially when integrated with ML, provides a practical and scalable approach for assessing LB severity in potato breeding programs while reducing the need for time-consuming field evaluations.

Net radiation (Rn) can be estimated using models that apply the Brunt equation for the incoming longwave radiation and air temperature (Tair) for the outgoing longwave radiation under reference conditions. This study aimed to estimate Rn using two previously regionally calibrated Brunt model, thereby eliminating the need site-specific calibration, and to assess whether Tair can be used as a substitute for canopy temperature (Tc) under well-watered crop conditions. Measurements were conducted in sesame and cotton fields during the first year and in a cotton field during the second year. Canopy temperature was measured during the second year, and the calculations were performed at hourly and daily time scales. Regardless of the method used to estimate sky emissivity or whether Tc or Tair was used, errors were greater at hourly time scale. The overall RMSE, MAE, Bias and KGE values at the daily time scales were 11.88, 9.13, 2.53, and 0.91, in the first year, and 13.45, 10.56, 0.10 and 0.74, in the second year, respectively. When using both regionally calibrated Brunt model, Rn simulation performance was superior to that of the Allen/FAO method. The comparison between Rn estimated using Tair and Tc, indicated statistical differences. Nevertheless, linear regression and error metrics showed that these differences were modest, especially at daily time scale. Thus, for practical purposes both regionally calibrated Brunt equations can be used to calculate clear-sky emissivity and improve Rn estimations, and Tair can be used as a substitute for Tc at the daily time scale under well-watered conditions.

O_LIAnatomical and histochemical imaging of grass root systems relies on tissue sectioning and cell wall staining dyes because molecular reporter lines are limited for most organisms. C_LIO_LIDistinct staining dyes require variable incubation time and concentration across different tissues and organisms. As a result, staining with multiple dyes becomes time consuming or challenging. Here, we report a rapid method to perform simultaneous triple staining on a glass slide. The entire protocol requires [~]4 hours and a smaller volume of stain than traditional methods. C_LIO_LIWe tested this method using the roots of two economically important crops, Triticum aestivum (wheat) and Zea mays (maize), as proof of concept. We have also demonstrated the presence of exodermis in wheat roots. Additionally, we identified the formation of polar lignin caps in maize exodermis using our simultaneous triple staining method. C_LIO_LIThis method empowers a quantitative approach to cell biology by elucidating cell-type specific spatio-temporal distribution of cell wall materials in monocot root systems. C_LI

Flow cytometry provides a reliable and fast method for estimating genome size and ploidy levels in plants. Until recently, most studies employed fresh tissues, which limits the use of the method with samples from remote areas or when an extremely high number of samples needs to be processed in a short time. Although there is growing evidence that silica-dried material can be used for ploidy estimation in some taxa, no flora-wide study has been available so far. Here, we provide methodological aspects of an unprecedented study exploring ploidy variation of non-apomictic angiosperms in the Eastern Alps. We have analysed ca. 45,000 silica-dried samples of 1135 species using flow cytometry with DAPI as stain. We were able to obtain ploidy level information from 1104 (97%) of species. The unsuccessful species included succulent plants of the family Crassulaceae (genera Jovibarba, Rhodiola, Sedum, Sempervivum), the achlorophyllous parasitic or mycoheterotrophic genera Orobanche and Hypopitis, and a handful of others. About 80% of samples were successfully analysed using a single universal protocol and leaf tissue, while in the remaining species the use of alternative tissues (such as petioles or flowers) and/or protocol modifications were needed (targeting composition of buffers, duration of fixation or staining time or use of alternative buffers). A total of 377 species (34%) included polyploid cytotypes and 179 (16%) species were ploidy-variable. As a community resource, we provide relative genome sizes and ploidy assignments of 1332 cytotypes retrieved from 1104 species along with methodological details (e.g. buffers, standards, analysed plant organs, histogram quality). We believe that this dataset will facilitate future research in particular species as well as in flora-wide investigations of ploidy level variation of the Central European flora in general. We are confident that novel cytotypes of many species will be discovered in other geographic areas, and we would be delighted if the present dataset could serve the botanical community for comparison.

Spinach (Spinacia oleraceae) is a principal vegetable crop commercially grown in Controlled Environment Agriculture (CEA). Recent research suggests that root morphological and architectural differences among crop species influence yield, resource use efficiency, and environmental stress tolerance. These root traits may be exploited to increase yield, promote efficient nutrient use, and mitigate environmental stressors. This study measured differences between various spinach cultivars in CEA systems to reveal morphological and anatomical variation. We grew three spinach cultivars with different reported growing rates ( Income, Darkside, and El-Majestic) under NFT hydroponic and substrate-based systems in a controlled greenhouse environment over 45 days with destructive harvests at days 15, 30, and 45. Supplemental light (250 {micro}mol/m2/s) with 12-hour photoperiod and periodic fertigation was used. Harvests included the collection of leaf and root biomass, and scanning of root systems in WinRhizo software, measuring ten variables. On day 45, root cross-sections from orders 1-5 were embedded in JB-4 resin, sectioned, stained, and analyzed for diameter, vasculature, and rhizodermis characteristics. Results indicate that in spinach, differences in root system morphology are linked to cultivation systems over cultivar identity. Vascular and root anatomical alterations are minor compared to morphological differences in response to the cultivation system. Hydroponic-style growth systems are associated with the proliferation of fine-root ideotypes compared with substrate-based conditions. Such findings affirm previous studies, which suggest plastic root morphology in response to growth systems, and may be used to help create more resilient, resource-efficient cultivars. HighlightsO_LIIn spinach, root system morphology differences are linked to cultivation systems. C_LIO_LIRoot vascular and anatomical alterations are minor in response to cultivation system. C_LIO_LIHydroponic growth systems are linked to fine-root ideotype proliferation in spinach. C_LIO_LIFine-root ideotype proliferation may be a breeding target for CEA spinach. C_LI

In soilless greenhouse tomato cultivation, daily transpiration and irrigation demand are largely governed by solar radiation, while irrigation-solution electrical conductivity (EC) used for salinity management may further modulate plant water use. This study developed a low-input, radiation-driven modeling approach to predict daily irrigation demand under contrasting water-salt management scenarios. Two tomato cultivars were grown under four treatments: conventional baselines (CK1, CK2) and regulated scenarios combining irrigation volume with solution EC (low-water high-EC, TK; high-water moderate-EC, TC). Daily irrigation volume (I) and drainage were recorded, and daily cumulative radiation (G) was derived from photosynthetically active radiation (PAR). Within each treatment, we compared a radiation-only baseline model with an EC-adjusted model and evaluated predictive performance using 5-fold blocked time-series cross-validation. Results showed strong positive correlations between G and I across all treatments (p < 0.001). The EC-adjusted models achieved cross-validated root-mean-square errors (RMSE) of 0.815-1.393 L d-1 per trough and Nash-Sutcliffe efficiencies (NSE) of 0.407-0.730. Incorporating EC yielded a small but consistent improvement under the TK scenario ({Delta}RMSE = -0.014 L d-1; {Delta}NSE = +0.019), whereas its effect was negligible or slightly negative under CK1, CK2, and TC, highlighting scenario dependence. Our radiation-driven framework, with an optional EC correction, offers a practical and scalable tool for daily irrigation forecasting and supports integrated water-salt management in soilless greenhouse tomato production.