Exploring an automated indoor high-throughput phenotyping facility to investigate the response of faba bean to water stress

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However, it is known to be very sensitive to droughts, which can severely impact yield. Uncovering drought-resilient germplasm is critical for developing resilient cultivars and advancing our understanding of the mechanisms underlying stress adaptation. However, reliable phenotyping of water stress responses remains a significant bottleneck in crop genetics and breeding programs. Overcoming this bottleneck requires high-throughput phenotyping platforms. In this study, we used an indoor image-based phenotyping facility, the National Plant Phenotyping Infrastructure at the University of Helsinki. The facility incorporates cutting-edge imaging technologies such as top- and side-view digital imaging for assessment of growth and development, as well as chlorophyll fluorometry for the detection of physiological responses. In this study, 44 faba bean accessions were subjected to early-stage water stress via weight-based water-holding capacity. The accessions presented a range of responses to water stress across the studied traits, including plant height, total canopy area, digital biomass, and water use efficiency. Our results also revealed a strong correlation between digital biomass and biological biomass. Here, we demonstrate the potential of a fully automated indoor phenotyping facility for screening a relatively large faba bean germplasm collection under well watered and water stressed conditions. Accessions that maintained growth and physiological performance under water stress conditions in this study may serve as valuable pre-breeding materials for the development of drought-adapted faba beans. drought plant phenomics germplasm survey digital biomass and water use efficiency Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Faba bean ( Vicia faba L.) is an important cool-season grain legume crop grown worldwide because of its high seed protein content and versatility in agricultural systems, contributing to food security and sustainable agriculture [ 1 , 2 ]. Nevertheless, faba bean is considered to be sensitive to droughts [ 3 ], the major environmental factor that affects its growth, development, and yield. Global climate change has dramatically changed the frequency and patterns of rainfall over the last century. The decrease in precipitation due to infrequent rain events and the predicted increase in atmospheric temperature increase the intensity and frequency of drought incidents, negatively affecting crop performance [ 4 , 5 ]. Drought remains the most important abiotic constraint hindering crop production globally [ 6 ]. For example, in the Nordic region, early- to mid-summer droughts have become extremely likely due to climate change and cause significant crop yield losses [ 7 ]. Therefore, breeding drought-adapted crop varieties is essential to improve yields under drought conditions, particularly in drought-susceptible crops such as faba bean. Plant phenotyping is the key component of plant agricultural research, which spans from understanding plant‒environment interactions to genetics, genomics studies and crop management [ 8 , 9 ]. Phenotypic characterization and identification of drought-adapted germplasms are essential for developing improved cultivars that can sustain productivity under water-limited conditions and help researchers understand the mechanisms underlying stress adaptation [ 10 ]. The screening of germplasm for drought adaptation involves evaluating diverse genetic resources under stress conditions to identify genotypes that maintain high productivity in water-limited environments [ 11 ]. A number of faba bean genotypes have been screened for this purpose [e.g., 12–14]. However, this process has been largely constrained by inadequate reliable and uniform phenotyping tools. High-throughput plant phenotyping (HTPP) has emerged as a transformative approach for studying drought adaptation in plants by enabling the rapid, precise, and large-scale assessment of germplasm under controlled and field conditions [ 15 – 17 ]. Leveraging advanced imaging technologies such as visible light RedGreenBlue (RGB) imaging, thermal imaging, hyperspectral imaging, and chlorophyll fluorometry, HTPP platforms provide invaluable insights into the physiological, morphological, and biochemical responses of plants to water stress [ 18 ]. These technologies not only allow the characterization of traits related to shoot and root growth and architecture but also facilitate the identification of genotypes with superior physiological adaptation to drought and enhance genetic studies where phenotyping and genotyping data are available [ 19 – 21 ]. Recent studies have demonstrated the effective application of unmanned aerial vehicles (UAVs) for high-throughput field phenotyping that integrates image analysis and machine learning in faba bean [ 22 , 23 ]. Balko et al. [ 24 ] employed a mobile field rainout shelter to screen 100 faba bean accessions under terminal drought conditions and identified the most drought-adapted genotypes. Field-based phenotyping is resource intensive and often suffers from low reproducibility of water stress conditions owing to the inherent variability in drought onset, duration, and intensity. GrowScreen-Rhizo boxes [ 25 ], a robotic root phenotyping tool, were employed for high-throughput phenotyping of faba bean roots [ 26 ]. To increase the knowledge of faba bean shoot responses to water stress, we opted to utilize an indoor HTPP phenotyping facility, the National Plant Phenotyping Infrastructure (NaPPI), University of Helsinki. NaPPI allows nondestructive, multimodal readouts of plant stress responses with visible light RGB cameras and chlorophyll fluorometry [ 27 – 29 ]. We characterized the early drought response of a Nordic–European faba bean germplasm collection under controlled water stress conditions via a state-of-the-art indoor phenotyping facility. Materials and methods Plant material Forty-four faba bean accessions were used in this study. Information on accession identification, origin and collection sites is presented in Table S1 . Most of the accessions were obtained from the common Nordic genebank NordGen (Alnarp, Sweden) and originated from Nordic countries and northern Europe. ILB 938/2 and Mélodie/2 were added to the set as benchmarks for high water use efficiency [ 30 , 31 ]. Growing conditions The plants were grown at the Viikki Plant Growth Facilities (ViGOR) at the University of Helsinki Viikki campus, Helsinki, Finland. Seeds were sown in three batches on October 3, 2024. Each batch contained two pots of all 44 accessions, one for the well watered treatment and one for the water deficit treatment. Before sowing, the seeds were inoculated with Rhizobium leguminosarum biovar. viciae (Elomestari Oy, Tornio, Finland). Sowing was performed in 5 L plastic pots filled with peat (Karkea Ruukutusseos, W R8014, Kekkilä Oy, Vantaa, Finland). The photoperiod was set to 16 h of light and 8 h of darkness, with temperatures maintained at 21°C during the day and 16°C at night. A photosynthetic photon flux density of approximately 250 µmol m⁻² s⁻¹ was maintained at the canopy level via high-pressure sodium lamps. The relative humidity was set at approximately 60%. Water stress treatment During the first seven days after germination (DAG), all the pots were watered to reach field capacity to allow proper seed germination. The plants were subsequently subjected to two distinct water regimes: 80% water holding capacity (WHC) as the control treatment and 30% WHC as the water stress treatment, which was progressively increased by 20 DAG (Fig. 1 ). The automated watering by the weighing facility allowed the watering levels of the water stress treatment to be maintained for 10 days. In the fourth week post-germination (31 DAG), the water status was adjusted, and the drought treatment was intensified to 20% WHC for 5 days. By the fifth week (35 DAG), the WHC was increased back to 30%, which was maintained until the end point of the harvest of fresh weight and dry matter at 39 DAG. Phenotyping The phenotyping was performed at the National Plant Phenotyping Infrastructure (NaPPI, https://www.helsinki.fi/en/infrastructures/national-plant-phenotyping ) facilities. The NaPPI Modular unit is a greenhouse facility that uses an automated conveyor system for plant movement and management for growth, weighing, watering and imaging. The programmable operations are based on individual plant registration and assignment to pots. In this experiment, the 264 plants were divided into three batches. Pots were labeled with tray numbers according to their accession identities and water treatment levels. Image-based phenotyping Imaging events were divided among the three batches over three days. In addition, all the plants were imaged together on one day. The imaging technologies used included visible light RGB sensors, RGB1 as a side-view line-scan camera and RGB2 as a top-view camera (IDS Imaging Development Systems GmbH, Obersulm, Germany). RGB imaging allows the capture of plant growth parameters such as the canopy area, side view area, perimeter, height and width (Figure S2 a and S2b). To facilitate top-view canopy data extraction, blue rubber mats were placed around each plant in each pot. For the side view images (RGB1), three photos were taken at each time point, with two 120° rotations of the plants. Any morphological parameters associated with the side views were calculated as the average of values derived from the three angles (0°, 120°, and 240°). Plant height was measured from the background excluded images by counting the distance from the lowest and highest pixels. The normalized plant height was calculated as follows: Normalized plant height = (plant height of WHC-30%/plant height of WHC-80%)-1 Chlorophyll fluorescence measurements Pulsed amplitude modulation (PAM) chlorophyll a fluorescence (CF) was used to record plant physiological responses during water stress. The maximum photochemical efficiency of photosystem II (PSII), the maximum quantum yield of PSII (QY max ), serves as an indicator of severe plant stress. During the final measurement, a quenching protocol was employed to determine extended ChlF parameters. This protocol allowed the acquisition of additional fluorescence metrics that characterize PSII photochemistry, including the steady-state quantum yield under light-adapted conditions (QY_Lss) and steady-state nonphotochemical quenching (NPQ). Biological biomass (End-point measurement) For the endpoint measurements at 39 DAG, the above-ground parts of the plants were collected into paper bags, and their fresh weights were recorded with a Mettler PM480 balance (GWB, Germany). The weight of each bag was subtracted from the net weight. After this, the plants were dried in a sample oven at 80°C, and the dry matter content was recorded on the same balance after two weeks. Calculated parameters Digital biomass The digital biomass was calculated as an indicator and quantitative estimator for fresh plant biomass. The extracted features, specifically the plant pixel area from three side and top view images, were combined to estimate a volume (unit: arbitrary volume, volume), referred to as “digital biomass”, which represents a pixel-based volumetric measurement [ 32 ] and is defined as: Digital biomass =√ (average side area) 2 × top area The normalized digital biomass data, recorded on the final day of the experiment, were calculated as follows: Normalized digital biomass = (digital biomass of WHC-30%/digital biomass of WHC-80%)-1 Water use efficiency (WUE) Daily watering by scaling allowed for the calculation of the total amount of water given to each plant from 4 to 39 DAGs. We determined the biological WUE by dividing the endpoint dry matter produced (g) by the total amount of water (kg) supplied until 39 DAG. The digital WUE was calculated by dividing the digital biomass (volume) by the total amount of water (kg) provided until 37 DAG, i.e., one day prior to imaging at 38 DAG. Data analysis The RGB images of each individual plant were subjected to fisheye correction and background exclusion. Data Analyzer v.3.4.17.3 (PSI, Drásov, Czech Republic) was used to manage the original images and data and their storage in the database. Canopy area, perimeter, height and width data were extracted from the green pixel measurements via MorphoAnalysis v.1.0.14.4 software (PSI, Drásov, Czech Republic). If necessary, filtering techniques were applied to identify irrelevant data points and to guide reanalysis settings to ensure that the numerical data match the ground truth. The CF data were analyzed via FluorCam 7 software (PSI.cz). The numerical data were processed via homemade pipelines via Python 3.13 ( https://www.python.org ). JMP Pro 18 was used for statistical analysis (two-way ANOVA) and data visualization, whereas the Seaborn Python library [ 33 ] was used to generate all plots. Results Plant height Water stress significantly reduced plant height for all the studied germplasms. The height reduction at 30% WHC is presented as the normalized plant height against the 80% WHC (Fig. 2 ). The results revealed that significant differences in plant height and water stress caused a significant reduction in plant height (Table S2 a), with some plants, such as Lövånger and Kärra, having the smallest reduction in height (Table S2 b; Fig. 2 ). Time series recordings of plant growth, such as plant height measurements, under the 80% and 30% WHC treatments revealed the different growth patterns between the two treatments as well as the different responses of the genotypes (Figure S2 ). Normalized digital biomass The digital biomass is an estimation of plant biomass. The top-view canopy area and the three side-view areas were used to calculate the digital biomass. All the accessions presented significant differences between the water stress treatments at the end of the experiment ( P < 0.001) (Table S2 a). Figure 3 shows the normalized digital biomass reduction at 30% WHC against 80% WHC for all of the accessions. Biological biomass WUE Biological WUE is one of the key indicators of drought adaptation and varied among the studied germplasm. The biological WUE was calculated as the ratio of dry weight to water used during the complete experiment. For example, ILB 938/2 produced the highest amount of biological biomass per kg of water used, and Mélodie/2 was ranked as the fourth most efficient in terms of biomass production per kg of water used under water stress conditions (Fig. 4 ; Table S2 b). All of these results show the ability of these accessions to increase their water use efficiency under water stress. Aurora/2, a known drought-susceptible faba bean, was among those with low biological biomass WUE under water stress conditions. Digital biomass WUE Similar to biological WUE, the digital WUE also varied among the 44 faba bean accessions (Table S2 b). Like the biological biomass WUE, ILB 938/2 was among those with high digital WUE under water stress conditions, and Aurora/2 was among those with the lowest digital WUE (Fig. 5 ). However, all the rankings of the accessions did not follow the same trend as the biological WUE. Relationships among the studied parameters Figure 6 shows the correlations among all morphophysiological parameters measured throughout the experiments. Some of the highlighted results are presented below. Relationship between digital biomass and biological biomass We measured both digital biomass (based on image-derived parameters) and biological biomass (by weighting), and these parameters were used to calculate the digital WUE and biological biomass WUE, respectively. There was a strong positive correlation between the digital biomass at the endpoint of the experiment and the biological dry weight under both growing conditions (Fig. 6 ). This relationship highlights the reliability of image-based digital biomass as a proxy for estimating plant growth responses. Relationship between digital WUE and biological biomass WUE A moderate positive correlation was observed between the digital WUE and biological biomass WUE under water stress conditions. This relationship was weaker under well-watered conditions (Fig. 6 ). Chlorophyll fluorescence (CF) imaging results CF imaging is an essential tool for evaluating photosynthetic efficiency in plants. Here, the CF was used to confirm the success of the water stress treatments. There were significant differences in the CF measurements among the 44 studied faba bean accessions (Table S2 a and S2b). Water stress caused a significant reduction in the effective quantum yield (QY_Lss) and an increase in nonphotochemical quenching (NPQ) parameters (Table S2 b). Relationship between the CF and other parameters Measuring fluorescence provides insights into the health and functionality of the photosynthetic apparatus in response to abiotic stresses such as water stress. The effective quantum yield (QY_Lss) was positively correlated with all growth parameters, such as plant height, area dimensions, and biomass. However, nonphotochemical quenching (NPQ), which estimates plant heat dissipation and increases under water stress, showed the opposite trend with respect to the growth parameters. WUE, which was negatively correlated with growth parameters, was also negatively correlated with QY_Lss but positively correlated with NPQ (Fig. 6 ). Discussion Phenotyping has long been a major bottleneck in effectively and precisely characterizing crop phenotypic diversity. Here, we present a case study in which a relatively large faba bean germplasm collection subjected to water stress was screened via an HTPP controlled environment phenotyping facility equipped with multiple imaging system phenotyping facilities. Our results characterize faba bean accessions that may be used as pre-breeding materials for breeding drought-adapted germplasms. We also show the efficiency of HTPP for morphophysiological trait screening. This study was carried out in a climate-controlled high-throughput phenotyping facility. HTPP platforms hold significant potential for accelerating breeding programs by enabling the efficient screening of genetic resources essential for agricultural sustainability under climate change [ 34 ]. Indoor HTPP systems, integrated with advanced imaging technologies, provide high-resolution, noninvasive monitoring of crop performance, allowing real-time data collection under controlled drought conditions. These advancements are further complemented by field-based phenotyping platforms that present realistic drought environments, facilitating trait discovery and validation. We used ILB 938/2 and Mélodie/2 as known genotypes with high WUE [ 13 , 35 , 36 ], and our results clearly revealed that these lines maintained high WUE under water stress conditions. This highlights the ability of the phenotyping facility we used to screen a germplasm collection uniformly and precisely. Droughts are becoming a major limiting factor for crop production globally. We need more climate-resilient crop varieties to be able to secure food and feed production. There is an urgent need for varieties that are better adapted to the changing climate and fluctuating weather conditions [ 37 , 38 ]. However, there has been less progress in improving drought adaptation in some crops, including faba beans [ 39 ]. To improve faba bean performance under drought conditions, understanding the response of plants to drought conditions is essential. As shown in the research here, high-throughput indoor phenotyping facilities can facilitate screening of germplasm collections for drought adaptation and enable the identification of additional traits for a more comprehensive characterization of stress responses. Rainout shelters are valuable tools in drought research, allowing controlled simulation field conditions to study crop responses and identify drought-adapted genotypes. Such facilities are available globally [e.g., 40, 41], and one has already been employed to study the response of faba bean to drought conditions [ 24 ]. Outdoor imaging phenotyping is limited by the challenge of capturing images within plots. As a result, biomass estimation often relies on the integration of complex geometric and spectral traits from UAVs [ 42 – 44 ] or the use of field robots equipped with specialized sensors such as LiDAR [ 45 – 47 ]. One key advantage of the indoor HTPP is the ability to use multi-side imaging, which enables more precise estimation of derived parameters, such as digital biomass. Digital biomass derived from digital top- and side-view images has been used not only to estimate plant biological biomass evolution in barley and maize (e.g., 48–50] but also to monitor the effects of biostimulants on drought stress in tomatoes [e.g., 29] and the salinity response in safflower [ 51 ]. Our results revealed a strong positive correlation between the digital and biological biomass at the endpoint, suggesting that digital biomass can serve as a method for assessing the plant growth responses of faba bean during drought stress. Furthermore, the digital biomass and precise recording of the watering events during the entire experiment allowed us to calculate the growth of the plants as a function of the amount of water provided. These parameters were named the digital WUE for increasing the digital biomass per kg of water given. These values were moderately correlated with the biological biomass WUE calculated in the same way but for the dry weight of the plants at the end of the experiment under water stress conditions (Fig. 6 ). WUE is a key trait in drought research, as it reflects a plant's ability to maintain productivity under limited water availability [e.g., 52]. Our results suggest that digital biomass and digital WUE may be effectively estimated using this infrastructure, particularly under water stress conditions. Conclusions This study investigated the early growth stage water stress response of a faba bean collection using an automated HTPP indoor phenotyping facility under controlled environmental conditions. Notably, drought adaptation is a complex trait that is highly influenced by genotype‒environment interactions. The indoor and outdoor germplasm screening activities are complementary. The accessions that maintained growth and physiological performance under water stress in this study need to be confirmed under field conditions, particularly yield. Our results also demonstrated that high-throughput indoor phenotyping facilities can effectively screen germplasm collections for drought adaptation-related traits and enable the identification of additional traits for a more comprehensive characterization of stress responses. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This project was funded by the Nordic Genetic Resource Center (NordGen), project ID 309203, and the Research Council of Finland, Academy projects, funding decision 363375 (Fabagen). Author Contribution SP: Investigation, Formal analysis, Writing – review & editing. UC-N: Funding acquisition, Resources. MO: Investigation. KH: Writing – original draft, Writing – review & editing, Supervision, Investigation, Conceptualization, Resources. HK: Writing – original draft, Writing – review & editing, Supervision, Investigation, Conceptualization, Funding acquisition. Acknowledgments We would like to thank Fereshteh Dehghani and Markku Tykkyläinen technical assistants of the glasshouse of the University of Helsinki, for their kind assistance during the experiments. Special thanks go to Dr. Matti Leino (Stockholm University, Sweden), Dr. Ingunn Vågen (NIBIO, Norway), and Dr. Gert Poulsen (Frøsamlerne, Denmark) in NordGen's working group on grain legumes for valuable suggestions regarding suitable accessions for the study. The project was initiated and carried out within the framework of the working group. We are also grateful to the staff of the seed laboratory at NordGen for preparing the main part of the seed material. Availability of data and materials The data will be made available upon request. All imaging and numerical data generated in this study will be deposited in PHIS (Phenotyping Hybrid Information System, http://www.phis.inra.fr/ ) , a platform designed for the organization, management, and sharing of plant phenotyping data. This approach aims to ensure highly structured data organization in alignment with the FAIR principles (Findable, Accessible, Interoperable, Reusable), thereby fully supporting open science practices. 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Identifying physiological and genetic determinants of faba bean transpiration response to evaporative demand. Ann Botany. 2023;131:533–44. https://doi.org/10.1093/aob/mcad006 . Bhat JA, Deshmukh R, Zhao T, Patil G, Deokar A, Shinde S, Chaudhary J. Harnessing high-throughput phenotyping and genotyping for enhanced drought tolerance in crop plants. J Biotechnol. 2020;324:248–60. https://doi.org/10.1016/j.jbiotec.2020.11.010 . Pixley KV, Cairns JE, Lopez-Ridaura S, Ojiewo CO, Dawud MA, et al. Redesigning crop varieties to win the race between climate change and food security. Mol Plant. 2023;16:1590–611. https://doi.org/10.1016/j.molp.2023.09.003 . Muktadir Md A, Adhikari KN, Merchant A, Belachew KY, Vandenberg A, Stoddard FL, Khazaei H. Physiological and biochemical basis of faba bean breeding for drought adaptation – a review. Agronomy. 2020;10:1345. https://doi.org/10.3390/agronomy10091345 . Ninomiya S. High-throughput field crop phenotyping: current status and challenges. Breed Sci. 2022;72:3–18. https://doi.org/10.1270/jsbbs.21069 . Li X, Xu X, Chen M, Xu M, Wang W, Liu C, Yu L, Liu W, Yang W. The field phenotyping platform's next darling: Dicotyledons. Front Plant Sci. 2022;13:935748. https://doi.org/10.3389/fpls.2022.935748 . Wang T, Liu Y, Wang M, Fan Q, Tian H, Qiao X, Li Y. Applications of UAS in crop biomass monitoring: A review. Front Plant Sci. 2021;12:616689. https://doi.org/10.3389/fpls.2021.616689 . Hütt C, Bolten A, Hüging H, Bareth G. UAV LiDAR metrics for monitoring crop height, biomass and nitrogen uptake: A case study on a winter wheat field trial. PFG – J Photogrammetry Remote Sens Geoinf Sci. 2023;91:65–76. https://doi.org/10.1007/s41064-022-00228-6 . Smith DTL, Chen Q, Massey-Reed SR, et al. Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location. Plant Methods. 2024;20:129. https://doi.org/10.1186/s13007-024-01236-w . Jimenez-Berni JA, Deery DM, Rozas-Larraondo P, Condon AG, Rebetzke GJ, James RA, Bovill WD, Furbank RT, Sirault XRR. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Front Plant Sci. 2018;9:237. https://doi.org/10.3389/fpls.2018.00237 . Colaço AF, Schaefer M, Bramley RGV. Broadacre mapping of wheat biomass using ground-based LiDAR technology. Remote Sens. 2021;13:3218. https://doi.org/10.3390/rs13163218 . Siebers MH, Fu P, Blakely BJ, Long SP, Bernacchi CJ, McGrath JM. Fast, nondestructive and precise biomass measurements are possible using lidar-based convex hull and voxelization algorithms. Remote Sens. 2024;16:2191. https://doi.org/10.3390/rs16122191 . Klukas C, Chen D, Pape J-M. Integrated analysis platform: An open-source information system for high-throughput plant phenotyping. Plant Physiol. 2014;165:506–18. https://doi.org/10.1104/pp.113.233932 . Chen D, Neumann K, Friedel K, Kilian B, Chen M, Altmann T, Klukas C, Neumann K, Klukas C, Friedel S, Rischbeck P, Chen D, Entzia A, Stein N, Graner A, Kilian B. (2015) Dissecting spatiotemporal biomass accumulation in barley under different water regimes using high-throughput image analysis. Plant Cell & Environment 38:1980–1996. https://doi.org/10.1111/pce.12516. Thoday-Kennedy E, Joshi S, Daetwyler HD, Hayden M, Hudson D, Spangenberg G, Kant S. Digital phenotyping to delineate salinity response in safflower genotypes. Front Plant Sci. 2021;12:662498. https://doi.org/10.3389/fpls.2021.662498 . Hatfield JL, Dold C. Water-use efficiency: Advances and challenges in a changing climate. Front Plant Sci. 2019;10:103. https://doi.org/10.3389/fpls.2019.00103 . Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2ab.xlsx FigureS12000x2146300dpi.tiff accessions and treatments are presented in Table S2b. Figure S1. (a) FishEyeMask images showing the side views of the Kokkosenkyla and Käarra genotypes under both well-watered (WHC-80%) and drought conditions (WHC-30%). (b) Background excluded images showing the top view canopy areas of the Sairalame0503 and Romfartuna genotypes under well watered (WHC-80%) and water stress conditions (WHC-30%). FigureS22000x2962300dpi.tiff Figure S2. Plant growth in the time series of the experiment as cm of height in the two water stress treatments. Blue line for growth on 80% WHC and red line for growth on 30% WHC. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6461902","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449362195,"identity":"bed449c8-abd5-4707-81fc-32a517be551b","order_by":0,"name":"Sylvain Poque","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Sylvain","middleName":"","lastName":"Poque","suffix":""},{"id":449362197,"identity":"b255a303-a26c-44eb-b2e3-51e5f8fc6694","order_by":1,"name":"Ulrika Carlson-Nilsson","email":"","orcid":"","institution":"NordGen","correspondingAuthor":false,"prefix":"","firstName":"Ulrika","middleName":"","lastName":"Carlson-Nilsson","suffix":""},{"id":449362198,"identity":"fd7e797f-ae4b-44e8-a9f8-2ba61b7ac4da","order_by":2,"name":"Muhammad Omer","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Omer","suffix":""},{"id":449362199,"identity":"495a39f9-5b70-41a3-b91a-0c565ca05c70","order_by":3,"name":"Kristiina Himanen","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Kristiina","middleName":"","lastName":"Himanen","suffix":""},{"id":449362201,"identity":"fe500d0d-20c7-4adb-90bb-2167719e79a4","order_by":4,"name":"Hamid Khazaei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIie3PMUsDMRTA8XfLdTma9WWxXyGlUBH8MHkInXoidLlBNFNcCq4dSv0KV4TOOQLXwcKtHRx6S6cWdLKDg+dVXCSno0P+EEJCfiQB8Pn+YaIaJgIp65VMDLQhUIESwMK/kZWB8Itw3UDgmwT6k1SzAuDKQU5bT2R2MLhk97bclLPnk5BZXU6uANsOcjaO02wKwxGavCdose2FSHfdVAA6/2Li1EaQkMpUH2lhSWOg+UbAjZMU+yN5sK23A03trWZZTdy3rOtbhpTmUR9IWRkCad74sPW++osY0HwVjVDmtquxIhOBblLEjy+75IJmxXL+eri2HcaWWz5+P8eOcpgj/LmFTed9Pp/P90sfQxxiH9dtDdoAAAAASUVORK5CYII=","orcid":"","institution":"University of Helsinki","correspondingAuthor":true,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Khazaei","suffix":""}],"badges":[],"createdAt":"2025-04-16 09:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6461902/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6461902/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81829936,"identity":"0d9b6d17-3c94-4856-a221-43a65c30b9ed","added_by":"auto","created_at":"2025-05-02 13:29:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":941871,"visible":true,"origin":"","legend":"\u003cp\u003eDaily watering levels of all 264 plants during the water stress treatment. Seven days after germination (DAG), watering was set to simulate water stress (orange dotted line) at a 30% water holding capacity (WHC), and the well watered control treatment (blue dotted line) was maintained at 80% WHC. The solid blue and orange lines indicate the pot weights before daily watering. The black dotted vertical line indicates the DAG when drought treatment was reached, the red dotted vertical line indicates when water stress treatment was set at 20% WHC, and the blue vertical dotted line indicates when stress was set back to 30% WHC.\u003c/p\u003e","description":"","filename":"Figure12000x742300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-6461902/v1/c0ed17e44555215232ad1c0e.png"},{"id":81831127,"identity":"96c0ff8b-1e53-40f0-a104-1d59ad3e59e4","added_by":"auto","created_at":"2025-05-02 13:45:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":497894,"visible":true,"origin":"","legend":"\u003cp\u003eAverage normalized plant height ±SD in response to water stress as height decreased at the end point (38 DAG) among 44 faba bean accessions.\u003c/p\u003e","description":"","filename":"Figure22000x483300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-6461902/v1/8ac230b7aee03d1be75622fb.png"},{"id":81828713,"identity":"24cbce6f-d979-465a-82e7-6784630fecde","added_by":"auto","created_at":"2025-05-02 13:13:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":520526,"visible":true,"origin":"","legend":"\u003cp\u003eAverage normalized digital biomass ±SD in response to water stress at the end point (38 DAG) among the 44 faba bean accessions.\u003c/p\u003e","description":"","filename":"Figure32000x483300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-6461902/v1/aa761ae81e85a2261dc0fab5.png"},{"id":81828714,"identity":"92393203-66b7-4612-a1b8-4072509e8948","added_by":"auto","created_at":"2025-05-02 13:13:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":808196,"visible":true,"origin":"","legend":"\u003cp\u003eAverage biological biomass water use efficiency ±SD, calculated as dry weight (g) divided by total water given (kg) to the plant until 39 DAG among 44 faba bean accessions under well watered (WHC 80%) and water stress treatments (WHC 30%).\u003c/p\u003e","description":"","filename":"Figure42000x896300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-6461902/v1/9c72624c33c7b3a35e243677.png"},{"id":81829356,"identity":"f7e213a4-3dec-45ac-94f3-b7389fbfb42f","added_by":"auto","created_at":"2025-05-02 13:21:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":836443,"visible":true,"origin":"","legend":"\u003cp\u003eAverage digital water use efficiency ±SD calculated as the digital biomass (volume) divided by the total water given (kg) to the plant until 38 DAG among 44 faba bean accessions under well-watered (WHC-80%) and water stress treatments (WHC-30%).\u003c/p\u003e","description":"","filename":"Figure52000x923300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-6461902/v1/c49e3633b811ef572853ea89.png"},{"id":81829359,"identity":"4e4b7436-4dfb-4c88-87b5-7f361d236c3e","added_by":"auto","created_at":"2025-05-02 13:21:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2707309,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix of the studied parameters. The values are Pearson’s correlation coefficients (\u003cem\u003er\u003c/em\u003e) for traits under well-watered (WHC-80%, top) and water-stress (WHC-30%, bottom) conditions. The average values of the measurements across accessions and treatments are presented in Table S2b.\u003c/p\u003e","description":"","filename":"Figure62000x1637300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-6461902/v1/79cb12975814c19fa4309910.png"},{"id":83743166,"identity":"04694edf-f723-49c9-ad69-f7a221eb1eec","added_by":"auto","created_at":"2025-06-02 02:16:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7139484,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6461902/v1/8deb3c71-644c-4bdf-bc84-daa29747a740.pdf"},{"id":81829344,"identity":"586ce0cf-1755-4acc-af0c-5548c93cddb5","added_by":"auto","created_at":"2025-05-02 13:21:52","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11378,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6461902/v1/668f6ef3985a3fe716b00ac7.xlsx"},{"id":81829348,"identity":"31d6aec9-b35d-4b53-9602-50323965744e","added_by":"auto","created_at":"2025-05-02 13:21:52","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":39907,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2ab.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6461902/v1/d91499441d86bb08853312f9.xlsx"},{"id":81828717,"identity":"855bdcbd-734e-4629-a8ce-f1b7338281e7","added_by":"auto","created_at":"2025-05-02 13:13:53","extension":"tiff","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1703092,"visible":true,"origin":"","legend":"\u003cp\u003eaccessions and treatments are presented in Table S2b.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S1. (a)\u003c/strong\u003e FishEyeMask images showing the side views of the Kokkosenkyla and Käarra genotypes under both well-watered (WHC-80%) and drought conditions (WHC-30%). (\u003cstrong\u003eb\u003c/strong\u003e) Background excluded images showing the top view canopy areas of the Sairalame0503 and Romfartuna genotypes under well watered (WHC-80%) and water stress conditions (WHC-30%).\u003c/p\u003e","description":"","filename":"FigureS12000x2146300dpi.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6461902/v1/aabc2ea34af8ebdcd090daff.tiff"},{"id":81828740,"identity":"ec86455a-a90f-42ad-8404-8a0d4ea2c06e","added_by":"auto","created_at":"2025-05-02 13:13:53","extension":"tiff","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":956902,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2.\u003c/strong\u003e Plant growth in the time series of the experiment as cm of height in the two water stress treatments. Blue line for growth on 80% WHC and red line for growth on 30% WHC.\u003c/p\u003e","description":"","filename":"FigureS22000x2962300dpi.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6461902/v1/a1321cc055fc744bf47985c2.tiff"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring an automated indoor high-throughput phenotyping facility to investigate the response of faba bean to water stress","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFaba bean (\u003cem\u003eVicia faba\u003c/em\u003e L.) is an important cool-season grain legume crop grown worldwide because of its high seed protein content and versatility in agricultural systems, contributing to food security and sustainable agriculture [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Nevertheless, faba bean is considered to be sensitive to droughts [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the major environmental factor that affects its growth, development, and yield. Global climate change has dramatically changed the frequency and patterns of rainfall over the last century. The decrease in precipitation due to infrequent rain events and the predicted increase in atmospheric temperature increase the intensity and frequency of drought incidents, negatively affecting crop performance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Drought remains the most important abiotic constraint hindering crop production globally [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For example, in the Nordic region, early- to mid-summer droughts have become extremely likely due to climate change and cause significant crop yield losses [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, breeding drought-adapted crop varieties is essential to improve yields under drought conditions, particularly in drought-susceptible crops such as faba bean.\u003c/p\u003e \u003cp\u003ePlant phenotyping is the key component of plant agricultural research, which spans from understanding plant‒environment interactions to genetics, genomics studies and crop management [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Phenotypic characterization and identification of drought-adapted germplasms are essential for developing improved cultivars that can sustain productivity under water-limited conditions and help researchers understand the mechanisms underlying stress adaptation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The screening of germplasm for drought adaptation involves evaluating diverse genetic resources under stress conditions to identify genotypes that maintain high productivity in water-limited environments [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A number of faba bean genotypes have been screened for this purpose [e.g., 12\u0026ndash;14]. However, this process has been largely constrained by inadequate reliable and uniform phenotyping tools.\u003c/p\u003e \u003cp\u003eHigh-throughput plant phenotyping (HTPP) has emerged as a transformative approach for studying drought adaptation in plants by enabling the rapid, precise, and large-scale assessment of germplasm under controlled and field conditions [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Leveraging advanced imaging technologies such as visible light RedGreenBlue (RGB) imaging, thermal imaging, hyperspectral imaging, and chlorophyll fluorometry, HTPP platforms provide invaluable insights into the physiological, morphological, and biochemical responses of plants to water stress [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These technologies not only allow the characterization of traits related to shoot and root growth and architecture but also facilitate the identification of genotypes with superior physiological adaptation to drought and enhance genetic studies where phenotyping and genotyping data are available [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Recent studies have demonstrated the effective application of unmanned aerial vehicles (UAVs) for high-throughput field phenotyping that integrates image analysis and machine learning in faba bean [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Balko et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] employed a mobile field rainout shelter to screen 100 faba bean accessions under terminal drought conditions and identified the most drought-adapted genotypes. Field-based phenotyping is resource intensive and often suffers from low reproducibility of water stress conditions owing to the inherent variability in drought onset, duration, and intensity. GrowScreen-Rhizo boxes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], a robotic root phenotyping tool, were employed for high-throughput phenotyping of faba bean roots [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo increase the knowledge of faba bean shoot responses to water stress, we opted to utilize an indoor HTPP phenotyping facility, the National Plant Phenotyping Infrastructure (NaPPI), University of Helsinki. NaPPI allows nondestructive, multimodal readouts of plant stress responses with visible light RGB cameras and chlorophyll fluorometry [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We characterized the early drought response of a Nordic\u0026ndash;European faba bean germplasm collection under controlled water stress conditions via a state-of-the-art indoor phenotyping facility.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material\u003c/h2\u003e \u003cp\u003eForty-four faba bean accessions were used in this study. Information on accession identification, origin and collection sites is presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Most of the accessions were obtained from the common Nordic genebank NordGen (Alnarp, Sweden) and originated from Nordic countries and northern Europe. ILB 938/2 and M\u0026eacute;lodie/2 were added to the set as benchmarks for high water use efficiency [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGrowing conditions\u003c/h3\u003e\n\u003cp\u003eThe plants were grown at the Viikki Plant Growth Facilities (ViGOR) at the University of Helsinki Viikki campus, Helsinki, Finland. Seeds were sown in three batches on October 3, 2024. Each batch contained two pots of all 44 accessions, one for the well watered treatment and one for the water deficit treatment. Before sowing, the seeds were inoculated with \u003cem\u003eRhizobium leguminosarum\u003c/em\u003e biovar. \u003cem\u003eviciae\u003c/em\u003e (Elomestari Oy, Tornio, Finland). Sowing was performed in 5 L plastic pots filled with peat (Karkea Ruukutusseos, W R8014, Kekkil\u0026auml; Oy, Vantaa, Finland). The photoperiod was set to 16 h of light and 8 h of darkness, with temperatures maintained at 21\u0026deg;C during the day and 16\u0026deg;C at night. A photosynthetic photon flux density of approximately 250 \u0026micro;mol m⁻\u0026sup2; s⁻\u0026sup1; was maintained at the canopy level via high-pressure sodium lamps. The relative humidity was set at approximately 60%.\u003c/p\u003e\n\u003ch3\u003eWater stress treatment\u003c/h3\u003e\n\u003cp\u003eDuring the first seven days after germination (DAG), all the pots were watered to reach field capacity to allow proper seed germination. The plants were subsequently subjected to two distinct water regimes: 80% water holding capacity (WHC) as the control treatment and 30% WHC as the water stress treatment, which was progressively increased by 20 DAG (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The automated watering by the weighing facility allowed the watering levels of the water stress treatment to be maintained for 10 days. In the fourth week post-germination (31 DAG), the water status was adjusted, and the drought treatment was intensified to 20% WHC for 5 days. By the fifth week (35 DAG), the WHC was increased back to 30%, which was maintained until the end point of the harvest of fresh weight and dry matter at 39 DAG.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePhenotyping\u003c/h3\u003e\n\u003cp\u003eThe phenotyping was performed at the National Plant Phenotyping Infrastructure (NaPPI, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.helsinki.fi/en/infrastructures/national-plant-phenotyping\u003c/span\u003e\u003cspan address=\"https://www.helsinki.fi/en/infrastructures/national-plant-phenotyping\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e facilities. The NaPPI Modular unit is a greenhouse facility that uses an automated conveyor system for plant movement and management for growth, weighing, watering and imaging. The programmable operations are based on individual plant registration and assignment to pots. In this experiment, the 264 plants were divided into three batches. Pots were labeled with tray numbers according to their accession identities and water treatment levels.\u003c/p\u003e\n\u003ch3\u003eImage-based phenotyping\u003c/h3\u003e\n\u003cp\u003eImaging events were divided among the three batches over three days. In addition, all the plants were imaged together on one day. The imaging technologies used included visible light RGB sensors, RGB1 as a side-view line-scan camera and RGB2 as a top-view camera (IDS Imaging Development Systems GmbH, Obersulm, Germany). RGB imaging allows the capture of plant growth parameters such as the canopy area, side view area, perimeter, height and width (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea and S2b). To facilitate top-view canopy data extraction, blue rubber mats were placed around each plant in each pot. For the side view images (RGB1), three photos were taken at each time point, with two 120\u0026deg; rotations of the plants. Any morphological parameters associated with the side views were calculated as the average of values derived from the three angles (0\u0026deg;, 120\u0026deg;, and 240\u0026deg;). Plant height was measured from the background excluded images by counting the distance from the lowest and highest pixels. The normalized plant height was calculated as follows:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNormalized plant height = (plant height of WHC-30%/plant height of WHC-80%)-1\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eChlorophyll fluorescence measurements\u003c/h2\u003e \u003cp\u003ePulsed amplitude modulation (PAM) chlorophyll a fluorescence (CF) was used to record plant physiological responses during water stress. The maximum photochemical efficiency of photosystem II (PSII), the maximum quantum yield of PSII (QY\u003csub\u003emax\u003c/sub\u003e), serves as an indicator of severe plant stress. During the final measurement, a quenching protocol was employed to determine extended ChlF parameters. This protocol allowed the acquisition of additional fluorescence metrics that characterize PSII photochemistry, including the steady-state quantum yield under light-adapted conditions (QY_Lss) and steady-state nonphotochemical quenching (NPQ).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eBiological biomass (End-point measurement)\u003c/h3\u003e\n\u003cp\u003eFor the endpoint measurements at 39 DAG, the above-ground parts of the plants were collected into paper bags, and their fresh weights were recorded with a Mettler PM480 balance (GWB, Germany). The weight of each bag was subtracted from the net weight. After this, the plants were dried in a sample oven at 80\u0026deg;C, and the dry matter content was recorded on the same balance after two weeks.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCalculated parameters\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eDigital biomass\u003c/h2\u003e \u003cp\u003eThe digital biomass was calculated as an indicator and quantitative estimator for fresh plant biomass. The extracted features, specifically the plant pixel area from three side and top view images, were combined to estimate a volume (unit: arbitrary volume, volume), referred to as \u0026ldquo;digital biomass\u0026rdquo;, which represents a pixel-based volumetric measurement [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and is defined as:\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDigital biomass =\u0026radic; (average side area)\u003csup\u003e2\u003c/sup\u003e \u0026times; top area\u003c/h2\u003e \u003cp\u003eThe normalized digital biomass data, recorded on the final day of the experiment, were calculated as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eNormalized digital biomass = (digital biomass of WHC-30%/digital biomass of WHC-80%)-1\u003c/em\u003e\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eWater use efficiency (WUE)\u003c/h2\u003e \u003cp\u003eDaily watering by scaling allowed for the calculation of the total amount of water given to each plant from 4 to 39 DAGs. We determined the biological WUE by dividing the endpoint dry matter produced (g) by the total amount of water (kg) supplied until 39 DAG. The digital WUE was calculated by dividing the digital biomass (volume) by the total amount of water (kg) provided until 37 DAG, i.e., one day prior to imaging at 38 DAG.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe RGB images of each individual plant were subjected to fisheye correction and background exclusion. Data Analyzer v.3.4.17.3 (PSI, Dr\u0026aacute;sov, Czech Republic) was used to manage the original images and data and their storage in the database. Canopy area, perimeter, height and width data were extracted from the green pixel measurements via MorphoAnalysis v.1.0.14.4 software (PSI, Dr\u0026aacute;sov, Czech Republic). If necessary, filtering techniques were applied to identify irrelevant data points and to guide reanalysis settings to ensure that the numerical data match the ground truth. The CF data were analyzed via FluorCam 7 software (PSI.cz). The numerical data were processed via homemade pipelines via Python 3.13 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.python.org\u003c/span\u003e\u003cspan address=\"https://www.python.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e JMP Pro 18 was used for statistical analysis (two-way ANOVA) and data visualization, whereas the Seaborn Python library [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] was used to generate all plots.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePlant height\u003c/h2\u003e \u003cp\u003eWater stress significantly reduced plant height for all the studied germplasms. The height reduction at 30% WHC is presented as the normalized plant height against the 80% WHC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results revealed that significant differences in plant height and water stress caused a significant reduction in plant height (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea), with some plants, such as L\u0026ouml;v\u0026aring;nger and K\u0026auml;rra, having the smallest reduction in height (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Time series recordings of plant growth, such as plant height measurements, under the 80% and 30% WHC treatments revealed the different growth patterns between the two treatments as well as the different responses of the genotypes (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eNormalized digital biomass\u003c/h2\u003e \u003cp\u003eThe digital biomass is an estimation of plant biomass. The top-view canopy area and the three side-view areas were used to calculate the digital biomass. All the accessions presented significant differences between the water stress treatments at the end of the experiment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the normalized digital biomass reduction at 30% WHC against 80% WHC for all of the accessions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBiological biomass WUE\u003c/h2\u003e \u003cp\u003eBiological WUE is one of the key indicators of drought adaptation and varied among the studied germplasm. The biological WUE was calculated as the ratio of dry weight to water used during the complete experiment. For example, ILB 938/2 produced the highest amount of biological biomass per kg of water used, and M\u0026eacute;lodie/2 was ranked as the fourth most efficient in terms of biomass production per kg of water used under water stress conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb). All of these results show the ability of these accessions to increase their water use efficiency under water stress. Aurora/2, a known drought-susceptible faba bean, was among those with low biological biomass WUE under water stress conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDigital biomass WUE\u003c/h2\u003e \u003cp\u003eSimilar to biological WUE, the digital WUE also varied among the 44 faba bean accessions (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb). Like the biological biomass WUE, ILB 938/2 was among those with high digital WUE under water stress conditions, and Aurora/2 was among those with the lowest digital WUE (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, all the rankings of the accessions did not follow the same trend as the biological WUE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eRelationships among the studied parameters\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the correlations among all morphophysiological parameters measured throughout the experiments. Some of the highlighted results are presented below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eRelationship between digital biomass and biological biomass\u003c/h2\u003e \u003cp\u003eWe measured both digital biomass (based on image-derived parameters) and biological biomass (by weighting), and these parameters were used to calculate the digital WUE and biological biomass WUE, respectively. There was a strong positive correlation between the digital biomass at the endpoint of the experiment and the biological dry weight under both growing conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This relationship highlights the reliability of image-based digital biomass as a proxy for estimating plant growth responses.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between digital WUE and biological biomass WUE\u003c/h2\u003e \u003cp\u003eA moderate positive correlation was observed between the digital WUE and biological biomass WUE under water stress conditions. This relationship was weaker under well-watered conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eChlorophyll fluorescence (CF) imaging results\u003c/h2\u003e \u003cp\u003eCF imaging is an essential tool for evaluating photosynthetic efficiency in plants. Here, the CF was used to confirm the success of the water stress treatments. There were significant differences in the CF measurements among the 44 studied faba bean accessions (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea and S2b). Water stress caused a significant reduction in the effective quantum yield (QY_Lss) and an increase in nonphotochemical quenching (NPQ) parameters (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eRelationship between the CF and other parameters\u003c/h2\u003e \u003cp\u003eMeasuring fluorescence provides insights into the health and functionality of the photosynthetic apparatus in response to abiotic stresses such as water stress. The effective quantum yield (QY_Lss) was positively correlated with all growth parameters, such as plant height, area dimensions, and biomass. However, nonphotochemical quenching (NPQ), which estimates plant heat dissipation and increases under water stress, showed the opposite trend with respect to the growth parameters. WUE, which was negatively correlated with growth parameters, was also negatively correlated with QY_Lss but positively correlated with NPQ (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePhenotyping has long been a major bottleneck in effectively and precisely characterizing crop phenotypic diversity. Here, we present a case study in which a relatively large faba bean germplasm collection subjected to water stress was screened via an HTPP controlled environment phenotyping facility equipped with multiple imaging system phenotyping facilities. Our results characterize faba bean accessions that may be used as pre-breeding materials for breeding drought-adapted germplasms. We also show the efficiency of HTPP for morphophysiological trait screening.\u003c/p\u003e \u003cp\u003eThis study was carried out in a climate-controlled high-throughput phenotyping facility. HTPP platforms hold significant potential for accelerating breeding programs by enabling the efficient screening of genetic resources essential for agricultural sustainability under climate change [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Indoor HTPP systems, integrated with advanced imaging technologies, provide high-resolution, noninvasive monitoring of crop performance, allowing real-time data collection under controlled drought conditions. These advancements are further complemented by field-based phenotyping platforms that present realistic drought environments, facilitating trait discovery and validation. We used ILB 938/2 and M\u0026eacute;lodie/2 as known genotypes with high WUE [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and our results clearly revealed that these lines maintained high WUE under water stress conditions. This highlights the ability of the phenotyping facility we used to screen a germplasm collection uniformly and precisely.\u003c/p\u003e \u003cp\u003eDroughts are becoming a major limiting factor for crop production globally. We need more climate-resilient crop varieties to be able to secure food and feed production. There is an urgent need for varieties that are better adapted to the changing climate and fluctuating weather conditions [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, there has been less progress in improving drought adaptation in some crops, including faba beans [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. To improve faba bean performance under drought conditions, understanding the response of plants to drought conditions is essential. As shown in the research here, high-throughput indoor phenotyping facilities can facilitate screening of germplasm collections for drought adaptation and enable the identification of additional traits for a more comprehensive characterization of stress responses. Rainout shelters are valuable tools in drought research, allowing controlled simulation field conditions to study crop responses and identify drought-adapted genotypes. Such facilities are available globally [e.g., 40, 41], and one has already been employed to study the response of faba bean to drought conditions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Outdoor imaging phenotyping is limited by the challenge of capturing images within plots. As a result, biomass estimation often relies on the integration of complex geometric and spectral traits from UAVs [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] or the use of field robots equipped with specialized sensors such as LiDAR [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne key advantage of the indoor HTPP is the ability to use multi-side imaging, which enables more precise estimation of derived parameters, such as digital biomass. Digital biomass derived from digital top- and side-view images has been used not only to estimate plant biological biomass evolution in barley and maize (e.g., 48\u0026ndash;50] but also to monitor the effects of biostimulants on drought stress in tomatoes [e.g., 29] and the salinity response in safflower [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Our results revealed a strong positive correlation between the digital and biological biomass at the endpoint, suggesting that digital biomass can serve as a method for assessing the plant growth responses of faba bean during drought stress. Furthermore, the digital biomass and precise recording of the watering events during the entire experiment allowed us to calculate the growth of the plants as a function of the amount of water provided. These parameters were named the digital WUE for increasing the digital biomass per kg of water given. These values were moderately correlated with the biological biomass WUE calculated in the same way but for the dry weight of the plants at the end of the experiment under water stress conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e). WUE is a key trait in drought research, as it reflects a plant's ability to maintain productivity under limited water availability [e.g., 52]. Our results suggest that digital biomass and digital WUE may be effectively estimated using this infrastructure, particularly under water stress conditions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study investigated the early growth stage water stress response of a faba bean collection using an automated HTPP indoor phenotyping facility under controlled environmental conditions. Notably, drought adaptation is a complex trait that is highly influenced by genotype‒environment interactions. The indoor and outdoor germplasm screening activities are complementary. The accessions that maintained growth and physiological performance under water stress in this study need to be confirmed under field conditions, particularly yield. Our results also demonstrated that high-throughput indoor phenotyping facilities can effectively screen germplasm collections for drought adaptation-related traits and enable the identification of additional traits for a more comprehensive characterization of stress responses.\u003c/p\u003e"},{"header":"Declarations","content":" \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis project was funded by the Nordic Genetic Resource Center (NordGen), project ID 309203, and the Research Council of Finland, Academy projects, funding decision 363375 (Fabagen).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSP: Investigation, Formal analysis, Writing \u0026ndash; review \u0026amp; editing. UC-N: Funding acquisition, Resources. MO: Investigation. KH: Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Supervision, Investigation, Conceptualization, Resources. HK: Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Supervision, Investigation, Conceptualization, Funding acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe would like to thank Fereshteh Dehghani and Markku Tykkyl\u0026auml;inen technical assistants of the glasshouse of the University of Helsinki, for their kind assistance during the experiments. Special thanks go to Dr. Matti Leino (Stockholm University, Sweden), Dr. Ingunn V\u0026aring;gen (NIBIO, Norway), and Dr. Gert Poulsen (Fr\u0026oslash;samlerne, Denmark) in NordGen's working group on grain legumes for valuable suggestions regarding suitable accessions for the study. The project was initiated and carried out within the framework of the working group. We are also grateful to the staff of the seed laboratory at NordGen for preparing the main part of the seed material.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe data will be made available upon request. All imaging and numerical data generated in this study will be deposited in PHIS (Phenotyping Hybrid Information System, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phis.inra.fr/\u003c/span\u003e\u003cspan address=\"http://www.phis.inra.fr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, a platform designed for the organization, management, and sharing of plant phenotyping data. This approach aims to ensure highly structured data organization in alignment with the FAIR principles (Findable, Accessible, Interoperable, Reusable), thereby fully supporting open science practices. The faba bean genetic resources used in this study are available for order from NordGen (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nordgen.org/our-work/genebank/seed-requests/\u003c/span\u003e\u003cspan address=\"https://www.nordgen.org/our-work/genebank/seed-requests/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJensen ES, Peoples MB, Hauggaard-Nielsen H. Faba bean in cropping systems. 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Front Plant Sci. 2019;10:103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2019.00103\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2019.00103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"drought, plant phenomics, germplasm survey, digital biomass and water use efficiency","lastPublishedDoi":"10.21203/rs.3.rs-6461902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6461902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFaba bean (\u003cem\u003eVicia faba\u003c/em\u003e L.) has great potential to contribute to sustainable agriculture and protein security globally. However, it is known to be very sensitive to droughts, which can severely impact yield. Uncovering drought-resilient germplasm is critical for developing resilient cultivars and advancing our understanding of the mechanisms underlying stress adaptation. However, reliable phenotyping of water stress responses remains a significant bottleneck in crop genetics and breeding programs. Overcoming this bottleneck requires high-throughput phenotyping platforms. In this study, we used an indoor image-based phenotyping facility, the National Plant Phenotyping Infrastructure at the University of Helsinki. The facility incorporates cutting-edge imaging technologies such as top- and side-view digital imaging for assessment of growth and development, as well as chlorophyll fluorometry for the detection of physiological responses. In this study, 44 faba bean accessions were subjected to early-stage water stress via weight-based water-holding capacity. The accessions presented a range of responses to water stress across the studied traits, including plant height, total canopy area, digital biomass, and water use efficiency. Our results also revealed a strong correlation between digital biomass and biological biomass. Here, we demonstrate the potential of a fully automated indoor phenotyping facility for screening a relatively large faba bean germplasm collection under well watered and water stressed conditions. Accessions that maintained growth and physiological performance under water stress conditions in this study may serve as valuable pre-breeding materials for the development of drought-adapted faba beans.\u003c/p\u003e","manuscriptTitle":"Exploring an automated indoor high-throughput phenotyping facility to investigate the response of faba bean to water stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-02 13:13:47","doi":"10.21203/rs.3.rs-6461902/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1dfede5e-22a1-43f9-90d6-f9990fc2d5b2","owner":[],"postedDate":"May 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T02:08:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-02 13:13:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6461902","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6461902","identity":"rs-6461902","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}