High-Throughput Phenotyping for Revealing Key Morpho-Physiological Traits for Drought Tolerance in Pea (Pisum sativum and Wild Relatives).

High‐Throughput Phenotyping for Revealing Key Morpho‐Physiological Traits for Drought Tolerance in Pea ( Pisum sativum and Wild Relatives) - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. 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Learn more: PMC Disclaimer | PMC Copyright Notice Physiol Plant . 2026 Apr 15;178(2):e70863. doi: 10.1111/ppl.70863 Search in PMC Search in PubMed View in NLM Catalog Add to search High‐Throughput Phenotyping for Revealing Key Morpho‐Physiological Traits for Drought Tolerance in Pea ( Pisum sativum and Wild Relatives) Maryam Bagheri Maryam Bagheri 1 CBQF – Centro de Biotecnologia e Química Fina – Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Porto, Portugal Find articles by Maryam Bagheri 1 , Rick van de Zedde Rick van de Zedde 2 Wageningen University & Research, Wageningen, the Netherlands Find articles by Rick van de Zedde 2 , Diego Rubiales Diego Rubiales 3 Institute for Sustainable Agriculture, CSIC, Córdoba, Spain Find articles by Diego Rubiales 3 , Carla S Santos Carla S Santos 1 CBQF – Centro de Biotecnologia e Química Fina – Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Porto, Portugal Find articles by Carla S Santos 1 , Marta W Vasconcelos Marta W Vasconcelos 1 CBQF – Centro de Biotecnologia e Química Fina – Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Porto, Portugal Find articles by Marta W Vasconcelos 1, ✉ Author information Article notes Copyright and License information 1 CBQF – Centro de Biotecnologia e Química Fina – Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Porto, Portugal 2 Wageningen University & Research, Wageningen, the Netherlands 3 Institute for Sustainable Agriculture, CSIC, Córdoba, Spain * Correspondence: Marta W. Vasconcelos ( mvasconcelos@ucp.pt ) ✉ Corresponding author. Revised 2026 Mar 15; Received 2025 Sep 10; Accepted 2026 Mar 16; Issue date 2026 Mar-Apr. © 2026 The Author(s). Physiologia Plantarum published by John Wiley & Sons Ltd on behalf of Scandinavian Plant Physiology Society. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMC Copyright notice PMCID: PMC13080420  PMID: 41982158 ABSTRACT Pea ( Pisum sativum ) production is challenged by drought stress. Traditional methods for assessing drought tolerance are limited, and high‐throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits. Herein, 180 Pisum spp. accessions were evaluated using an indoor HTP platform under two irrigation treatments, control (70% field capacity) and drought stress (30% field capacity), for 50 days. A combination of digital phenotyping via imaging and manual measurements was used to analyse biomass‐related, architectural, and physiological traits. Drought conditions resulted in significant reductions in biomass‐related traits including fresh weight (47%), total leaf area (43%), and dry weight (41%). In contrast, PSII photochemical efficiency, leaf weight ratio, and solidity showed negative sensitivity index values (ranging from −7% to −1%), indicating comparatively lower sensitivity to drought and suggesting relative stability of these traits under water‐limited conditions. The high heritability value for water use efficiency (0.87) suggests that this parameter may be useful for distinguishing pea's responses to suboptimal soil moisture levels. Principal component analysis (PCA) highlighted patterns of trait variation and associations among biomass‐related traits, such as fresh weight, dry weight, and leaf area, which were sensitive to drought conditions. This suggests that the plants may use a combination of strategies to cope with water limitations. Furthermore, studying the significant variation in drought response among the diverse Pisum species and subspecies revealed distinct adaptation strategies. These findings support the development of crops that are resilient to the negative effects of climate change. Keywords: architectural traits, drought sensitivity, heritability, pea collection, photosynthetic traits, plant phenotyping, stress sensitivity index 1. Introduction In 2024, the global production of peas ( Pisum sativum ) was approximately 13–16 million tons of dry peas and 21 million tons of green peas (FAOSTAT 2024 ). As a widely cultivated legume, peas are a crucial protein source for both human diets and animal feed. Various factors influence production levels, including environmental conditions, market demand, and agricultural practices (Thavarajah et al. 2023 ). Drought stress is increasingly impacting pea production, particularly in southern and eastern Europe, where climate change has intensified. Research indicates that extended periods of low rainfall, combined with rising temperatures, have led to significant yield declines in critical agricultural regions, such as Spain, Italy, and Greece (Vicente Serrano et al. 2020 ; Pinke et al. 2024 ). For instance, recurring drought in southern Spain has resulted in limited water availability, adversely affecting vegetation and crop yields (Espinosa‐Tasón et al. 2022 ). Similar trends are evident in Eastern Europe, particularly in Romania and Hungary, where worsening drought conditions further affect crop production (Olesen et al. 2011 ). Drought stress occurs when water availability is insufficient to meet the plant's physiological needs, resulting in a destructive water deficit (Farooqi et al. 2020 ). In peas and other legumes, drought stress significantly hinders growth and development, leading to notable yield declines (Abdel‐Hamid and Salem 2021 ). Drought stress induces a wide range of physiological responses in pea plants that are essential for their survival but are often detrimental to growth and productivity. These responses typically include reduced cell division and proliferation, inhibition of stem elongation and leaf growth, dysregulation of stomatal regulation, and reduced nutrient uptake efficiency (Nadeem et al. 2019 ). Collectively, these changes reduce water use efficiency (WUE), which represents the plant's ability to convert water into biomass, ultimately leading to reduced crop yield and compromising agricultural performance under water‐limited conditions (Khatun et al. 2021 ). Therefore, reducing biomass accumulation in legumes such as cowpea (Santos et al. 2020 ) and common bean (Padilla‐Chacón et al. 2017 ) is considered as a strategy in response to water limitation. Furthermore, photosynthesis is a critical process for growth and production and also sensitive to drought stress and reduced water availability, leading to noticeable changes in photosynthetic activity and biomass production in legumes (Juzoń et al. 2019 ). To ensure stable pea production under increasingly unpredictable weather patterns, research and adaptation strategies are essential. Wild P. sativum representatives, including P. sativum subsp. elatius and P. fulvum , offer greater genetic diversity compared to cultivated species, which can ensure adaptation to a wide range of stresses and improve the objectives of pea breeding programs (Smýkal et al. 2018 ). As such, breeding programs have gradually focused on developing drought‐tolerant pea varieties while implementing improved irrigation and water management strategies (Bagheri et al. 2023 ). Marker‐assisted recurrent selection (MARS) and genomic selection have been applied to develop drought‐tolerant pea varieties (Iglesias‐García et al. 2015 ; Smýkal et al. 2016 ). However, these methods exceedingly depend on accurate phenotyping, which remains a significant challenge due to its labor‐intensive and time‐consuming nature (Khuimphukhieo and da Silva 2025 ). To address these issues, high‐throughput phenotyping (HTP) technologies are being investigated as a modern means to accelerate these processes (Cobb et al. 2013 ; Gondalia et al. 2022 ). High‐throughput phenotyping has the potential to accelerate the development of drought‐adapted pea cultivars, offering rapid, accurate, and automated data collection methods (Yang et al. 2020 ). This approach enables the simultaneous evaluation of numerous genotypes, facilitating early screening of important traits and drastically reducing the time and labor traditionally required for phenotypic evaluations (Yang et al. 2020 ; Sheikh et al. 2024 ). Advanced imaging technologies—including hyperspectral and thermal imaging—provide real‐time tracking of stress indicators, such as leaf temperature and water content, allowing the identification of drought‐tolerant genotypes (Kim et al. 2021 ). High‐throughput phenotyping platforms have previously demonstrated effectiveness in assessing drought tolerance in various crops, including maize, quinoa (Jaramillo Roman et al. 2021 ), and wheat, and are now being applied to legumes like peas to evaluate growth dynamics, such as plant height, biomass, chlorophyll content, and convex hull area (Asaari et al. 2019 ; Correia et al. 2022 ). Under drought stress, plants typically have smaller canopies and fewer foliage, which significantly reduces the convex hull area, which has been previously reported in faba bean (Belachew et al. 2018 ), rice (Vishal et al. 2020 ), and maize (Dar et al. 2021 ). Vegetation indices obtained from images have previously been used to assess the effects of drought stress in legumes such as common bean (Javornik et al. 2023 ). Some indices, particularly the Normalized Difference Vegetation Index (NDVI), have been effective in showing drought‐induced reductions in vegetation vigor and chlorophyll content (Trapp et al. 2016 ). Other pigment‐based indices like the normalized pigment chlorophyll ratio index (NPCI) and the plant senescence reflectance index (PSRI) reflect leaf pigment composition and accelerated senescence under stress conditions (Saglam et al. 2011 , Farias et al. 2023 ). Furthermore, the green leaf index (GLI) has demonstrated a strong correlation with biomass and canopy cover, and is reduced under water‐limited conditions in legumes (Javornik et al. 2023 ). Despite the advantages of these indices, there remains a need to better understand and identify relationships among traits in the large‐scale drought‐tolerance screening of Pisum germplasm. The large volume of phenotypic data generated through HTP can be integrated with genomic information, including quantitative trait locus (QTL) mapping, to enhance the selection for drought‐tolerant genotypes in breeding programs (Bhat et al. 2020 ). This combination enables accurate identification of genotype–phenotype correlations, which are critical for efficiently selecting traits associated with drought tolerance in legumes such as pea varieties (Bhat and Yu 2021 ). This study aims to utilize digital imaging techniques in conjunction with manually recorded data to evaluate morphophysiological traits in 180 pea accessions under well‐watered (control) and drought‐stress conditions. Automated high‐throughput screening methods were employed to identify sensitive and resistant genotypes and to identify the most relevant traits associated with drought tolerance in pea plants. Specifically, the research will analyse the effects of drought‐stressed (DS) conditions through image‐based and manual trait assessments and the variable responses of accessions in terms of biomass‐related, architectural, and physiological traits as recorded by both digital imaging techniques and manual observations. The overarching ambition is to build a showcase and offer proof/trust for pea breeders to rely more on digital plant phenotyping technologies in their breeding programs and enable them to scale up their capacity to screen many genotypes than current practices. 2. Materials and Methods 2.1. Plant Material and Growth Conditions A controlled pot experiment was conducted from March 6 to April 24, 2023, at the Netherlands Plant Eco‐phenotyping Centre (NPEC) greenhouse, Wageningen University and Research in the Netherlands. The study involved 180 Pisum spp. accessions [including Pisum fulvum ( n = 6), P. abyssinicum ( n = 2), and various subspecies of P. sativum , namely jomardii ( n = 56), arvense ( n = 47), sativum ( n = 37), humile ( n = 10), elatius ( n = 3), and the Indian ecotype of P. sativum ( n = 19)]. Two irrigation treatments were applied: control and drought‐stressed conditions, each replicated three times, resulting in a total of 1080 experimental units. Each plant was grown individually in an approximately 3 L pot (17 cm in diameter and 13 cm in height), filled with potting soil PG MIX 15 + 10 + 20 (Yara Nederland). The pots were placed on conveyor belts within the greenhouse, following a completely randomized design (Figure 1 ). Greenhouse environmental conditions were strictly controlled, with daytime temperatures maintained at 22°C and night‐time temperatures at 18°C. Relative humidity was set at 60%, and the photoperiod was set to 16 h light. FIGURE 1. Open in a new tab Randomized pot arrangement for control and drought treatments. (A) The pots were placed on conveyor belts, following a completely randomized design, with each rectangular grid representing a pot. Blue ones represent plants grown under control conditions, and orange ones represent those grown under drought conditions. (B) Practical pot arrangement in the greenhouse. During the initial growth phase, all pots were watered regularly to maintain 70% field capacity (FC) for both treatment conditions. Drought stress was applied after two weeks, once the pea seedlings had developed four true leaves. At this time, irrigation levels were adjusted to maintain 70% FC for the control group and 30% FC for the drought treatment conditions. Pots were weighed every other day to monitor water needs and ensure precise irrigation levels. 2.2. Image‐Derived Plant Traits HTP was conducted using multiple imaging techniques: RGB side‐view imaging, spectral imaging (SI), and top‐view chlorophyll fluorescence (CF) imaging. The RGB Sideview imaging system, provided by the company SMO BV from Belgium, is equipped with a high‐resolution Allied Vision GT3400C RGB camera, a progressive‐scan‐CCD sensor with bayer filter; 3384 (H) × 2704 (V) pixel with 3.69 μm pixel size. Plants entering the RGB sideview cabinet were stopped on a turn‐table. The rotation of the table was set to make an image at six different angles (0, 60, 120, 180, 240, and 300 degrees). Hence, six images were captured for each plant at each measurement. Each image was masked for plant parts and analysed plant parameters, such as digital biomass, convex hull, height, and solidity. Sideview digital biomass was calculated as the average of six sideview projected areas of plant parts, while height was measured using the pixel height of the plants. The convex hull, which is the smallest area that encloses the entire plant, provides information about the overall shape and size of plant structures. Solidity is the ratio of digital biomass to the convex hull area and represents the compactness of plant structures. The value of solidity ranges from 0 to 1, with higher values indicating a more compact or solid shape. The analysis software for the RGB side‐view imaging has been developed in Python by the NPEC data team, the source is published on Github, accessible via this link: https://github.com/NPEC‐NL/greenhouse_m5 . The topview SI and CF images were captured using the CropReporter camera systems (PhenoVation B.V.). The CropReporterTM consists of a cabinet with a camera system that houses a controller computer, a charge‐coupled device (CCD) camera with an optical filter wheel and a focusing unit with integrated high‐intensity red light‐emitting diodes (LEDs) for excitation of the photosynthesis. All images are captured with the same lens (10 MP 8 mm lens, 200 Lp mm −1 resolution, 400–1000 nm spectral range) and CCD camera (1.3 MP, 1296 × 996 pixels), with real 14‐bit signal resolution. Plants were imaged at approximately 140 cm distance from the camera. The output is 16‐bit RAW format. Before the CF measurements, plants were dark‐adapted for 30 min. For the excitation of photosynthesis, 4000 μmolm −2 s −1 red LED light was used. The integration time for capturing the chlorophyll fluorescence image was 200 μs. The minimum chlorophyll fluorescence (F0) and maximum chlorophyll fluorescence (Fm) images were captured after 10 μs and 800 ms, respectively. Automatic analysis of SI, and CF imaging was performed by Data Analysis (DA)TM software developed by PhenoVation B.V., Wageningen, The Netherlands, which is extensively documented on their website, accessible via https://www.phenovation.com/da‐tutorials . 2.2.1. Imaging and Key Traits RGB Imaging: RGB images were captured within an imaging cabinet that can rotate pots 360° while making RGB side view pictures from the plant on the turn table equipped with a blue background and LED lighting. The rotation is used to make pictures with different viewing angles. Six side‐view images were taken per plant using a progressive scan CCD sensor (3384 × 2704 pixels). Key traits derived from these images included: Side‐View Digital Area (SA): Calculated as the average of six side‐view projected areas of plant parts. Convex Hull (CH): The smallest area enclosing the entire plant, providing insights into plant structure and size. Digital Biomass (DB): Estimated by averaging the segmented plant profiles from the six images with six different angles. Solidity (SOL): Representing plant compactness, calculated as the ratio of DB to CH area. Chlorophyll fluorescence imaging and spectral imaging measurements were taken using the CropReporter systems (PhenoVation B.V.). These top‐view images were captured with an integrated CCD camera. Key traits are: PSII Photochemical Efficiency (PhE) (Fv/fm): Fv/fm was recorded after the plants were dark‐adapted for 30 min and was used as an indicator of the maximum photochemical efficiency of photosystem II and stress‐related photoinhibition, rather than a direct measure of overall photosynthetic performance. Water Use Efficiency (WUE) was calculated using the formula: WUE = Digital biomass water used Several physiological parameters were approximated or calculated using only spectral reflectance in red (RRed), spectral reflectance in green (RGreen), and spectral reflectance in blue (RBlue) derived by spectral imaging (SI): Normalized Difference Vegetation Index (NDVI): Used to estimate photosynthetic efficiency, approximated as (Gitelson and Merzlyak 1998 ) NDVI ≈ RGreen − RRed RGreen + RRed Normalized Pigment Chlorophyll Ratio Index (NPCI): Estimated chlorophyll content using the formula (Merzlyak et al. 1999 ) NPCI = RRed − RBlue RRed + RBlue Plant Senescence Reflectance Index (PSRI): Representing leaf senescence and calculated as (Peñuelas et al. 1994 ) PSRI = RRed − RBlue RGreen Green Leaf Index (GLI): Quantified green parts of the plant using (Louhaichi et al. 2001 ) GLI = 2 × RGreen − RRed − RBlue 2 × RGreen + RRed + RBlue 2.3. Traits Collected Manually Fifty days after the start of the experiment, the pea plants were harvested at the soil level to measure their fresh weight (FW). Dry weights (DW) were recorded after drying the samples in an oven set to 105°C for 72 h. The total leaf area (TLA) was measured using an LI‐3100C area meter (LI‐COR). Plant height (PH) was recorded in centimetres. Based on these measurements, several key ratios were calculated as follows: Specific Leaf Area (SLA): the ratio of leaf area to leaf dry weight, expressed in cm 2 g −1 DW. Leaf Weight Ratio (LWR): The ratio of leaf dry weight to the total plant dry weight, expressed in g g −1 DW. Leaf Area Ratio (LAR): The ratio of leaf area to total plant dry weight, expressed in cm 2 g −1 DW. The Relative Water Content (RWC) was determined using the formula: R WC = Fresh weight − Dry weight Saturated fresh weight − Dry weight × 100 The sensitivity index (SI) of each trait to drought condition was calculated using this formula. SI = X ¯ c − X ¯ s X ¯ c × 100 where X ¯ c = Mean trait value under drought stress, X ¯ s = Mean trait value under control condition. 2.4. Statistical Analysis Statistical analyses and data visualizations were conducted using the R programming environment (R Core Team 2022 ). A two‐way ANOVA was performed for a completely randomized design, followed by Tukey's protected least significant difference (LSD) test to evaluate the statistical significance of differences in trait means among treatments, with a threshold p ‐value of < 0.05. The measures of descriptive statistics, box‐plot as well as the Person linear correlation coefficients were used to analyze observed traits. Furthermore, a correlation network map based on the matrix of Person linear coefficients was constructed to visually identify correlation patterns that are not observable in a symmetric correlation matrix (Ursem et al. 2008 ). In a correlation network map, the traits represent variables as nodes that are connected by edges, whose width is proportional to the strength of the correlation. Based on the REML estimates of the variance components of random terms, the sample‐basis heritability ( h 2 ) of the traits was estimated using the formula equation computed by Holland et al. ( 2003 ). The relationships between traits were further analysed using Pearson correlation coefficients and principal component analysis (PCA) to assess multicollinearity within the dataset. 3. Results 3.1. Trait Performance Under Drought Stress Overall, the control conditions consistently yielded the highest means for most of the studied traits (Table 1 ). However, exceptions were observed: the drought conditions led to decreases in LWR for biomass‐related traits, as well as in PhE and GLI for physiology‐related traits, and in Sol for architectural traits, compared to the control conditions (Table 1 ). The Sensitivity Index (SI) further revealed the variations in the responses of different traits to drought stress, with both image‐based measures and manual assessments indicating notable drought‐induced effects (Table 1 ). TABLE 1. Descriptive statistics (mean) and sensitivity indices (SI) to drought for image‐based and manually measured traits of 180 pea accessions grown under control and drought stress conditions. Traits Control (mean) Drought (mean) SI (%) Image‐based traits DB (Digital biomass) 278.17 188.49 32.24 CH (Convex hull area) 2081.44 1367.50 34.30 Sol (Solidity) 0.15 0.16 −5.77 NPCI (Normalized pigment chlorophyll ratio index) 0.18 0.14 19.8 GLI (Green leaf index) 0.29 0.27 6.93 NDVI (Normalized difference vegetation index) 0.21 0.20 2.86 PSRI (Plant senescence reflectance index) 0.19 0.16 17.28 TA (Top area) 104.58 98.71 5.60 SA (Side area) 173980.09 117979.18 32.18 PhE (PSII photochemical efficiency) 0.77 0.78 −0.94 WUE (Water use efficiency) 2214.52 1691.62 23.61 Manual traits FW (Fresh weight) 23.29 12.32 47.10 DW (Dry weight) 4.11 2.41 41.36 PH (Plant height) 123.26 91.44 25.81 TLA (The total leaf area) 818.14 466.32 43.00 SLA (Specific leaf area) 243.53 219.53 9.85 LWR (Leaf weight ratio) 0.30 0.32 −6.66 LAR (Leaf area ratio) 200.30 197.73 1.28 RWC (Relative water content) 75.41 63.348 15.99 Open in a new tab Drought stress reduced several traits associated with biomass accumulation (Table 1 ). Among the manually measured traits, FW decreased by approximately 47%, DW by 41%, TLA by 43%, and PH by 26% (Table 1 ). Similarly, image‐based traits reflected comparable trends, with DB decreasing by about 32%, CH by 34%, and SA by 32% (Table 1 ). In contrast, negative sensitivity index (SI) values were observed in three traits: LWR (−6.66%), Sol (−5.77%), and PhE (−0.94%; Table 1 ). Analysis of variance disclosed significant effects of both genotype and growth conditions on multiple plant traits (Table S2 and Figure S1 ). All measured traits showed highly significant genotype effects ( p < 0.001, except WUE at p < 0.01). Different growth conditions (control and drought) also strongly influenced all traits ( p < 0.001, except TA at p < 0.05). Importantly, significant genotype × growth‐condition interactions were detected for most traits ( p < 0.001), except for GLI and WUE. Considering the coefficient of variation (CV), most of the studied traits showed relatively high variability with at least 25% in each condition (Table S3 ). Low (5%–10%) and moderate (10%–20%) variability was found within treatments in RWC, GLI, and NDVI, while very low variability (< 5%) within each condition was observed only in PhE. For most traits, CV was similar across conditions, with slight increases under drought stress. However, fresh FW, DW, and LWR were exceptions, showing lower CV under drought conditions compared to control conditions (Table S3 ). The studied traits showed variable effects of genotype and growth conditions, as well as their interactions (Figure 2 and Table S4 ). Genotype had the greatest impact on all traits compared to growth condition or their interaction, with the strongest effects observed on water use efficiency (WUE) at 92.5%, solidity (SOL) at 90.4%, green leaf index (GLI) at 83.9%, and leaf area ratio (LAR) at 68.7%. The effect of growth conditions was lesser; however, the highest effect of conditions was observed in some traits related to biomass: relative water content (RWC; 41.8%), fresh weight (FW; 39.0%), digital biomass (DB; 32.4%), dry weight (DW; 30.6%), and side‐view digital area (SA; 30.3%). The interaction effect was dominant in PSII photochemical efficiency (PhE; 36.3%), leaf weight ratio (LWR; 33.1%) as well as in some architectural features such as leaf area ratio (LAR; 31.2%) and total leaf area (TLA; 27%). Heritability ( h 2 ) was generally higher for image‐based traits than for manually measured traits (Table S4 ). The highest heritability (> 0.90) was for solidity (SOL) and water use efficiency (WUE). The lowest heritability (< 0.50) was obtained for some biomass‐related traits (which were manually collected) such as relative water content (RWC) (0.42), fresh weight (FW; 0.44) and dry weight (DW; 0.47). FIGURE 2. Open in a new tab Relative contribution (%) of accessions, conditions, and their interaction on traits. Relative contribution of the variance components to the phenotypic variance of image‐based [Digital Biomass (DB), PSII Photochemical Efficiency (PhE), Top‐area (TA), Side‐View Digital Area (SA), Convex Hull (CH), Solidity (SOL), Water Use Efficiency (WUE), Normalized Difference Vegetation Index (NDVI), Normalized Pigment Chlorophyll Ratio Index (NPCI), Plant Senescence Reflectance Index (PSRI), Green Leaf Index (GLI)] and manually measured [fresh weight (FW), Dry weights (DW), The total leaf area (TLA), Plant height (PH), Specific Leaf Area (SLA), Leaf Weight Ratio (LWR), Leaf Area Ratio (LAR), Relative Water Content (RWC)] traits. 3.2. Trait Correlations Under Drought and Control Conditions Networks visualizing phenotypic ( r ) correlations among all studied traits in the “Control Condition” and “Drought Condition” are given in Figure 3 and Tables S5 and S6 . FIGURE 3. Open in a new tab Phenotypic correlation networks of image‐based and manual plant traits under control and drought conditions. Visualization of phenotypic correlation networks showing relationships between image‐based [Digital Biomass (DB), PSII Photochemical Efficiency (PhE), Top‐area (TA), Side‐View Digital Area (SA), Convex Hull (CH), Solidity (SOL), Water Use Efficiency (WUE), Normalized Difference Vegetation Index (NDVI), Normalized Pigment Chlorophyll Ratio Index (NPCI), Plant Senescence Reflectance Index (PSRI), Green Leaf Index (GLI)] and manually measured [fresh weight (FW), Dry weights (DW), The total leaf area (TLA), Plant height (PH), Specific Leaf Area (SLA), Leaf Weight Ratio (LWR), Leaf Area Ratio (LAR), Relative Water Content (RWC)] traits, under control (a) and drought (b) conditions. Green, orange, and purple nodes represent traits related to biomass, architectural features, and physiological traits, respectively. Red connecting lines indicate negative correlations, dark red indicates strong negative correlations, and light red indicates weak negative correlations. Blue connecting lines indicate positive correlations, where dark blue indicates strong positive correlations and light blue indicates weak positive correlations. In control conditions, among the manually measured traits, the highest positive correlations were observed between fresh weight (FW) and two other biomass‐related traits: dry weight (DW) and relative water content (RWC; r = 0.91 and 0.81, respectively), as well as dry weight (DW) with relative water content (RWC; r = 0.71). Almost the same trend was obtained for drought conditions, where fresh weight (FW) and relative water content (RWC) had a strong positive correlation ( r = 0.69). However, in drought conditions, plant height (PH) had a significant relationship with convex hull (CH; r = 0.73), while it was not observed in the control conditions. Among the measurements obtained from the image, in both conditions, the highest correlation was shown between the two physiological traits plant senescence reflectance index (PSRI) and normalized pigment chlorophyll ratio index (NPCI; r ≤ 0.98 ), followed by digital biomass (DB) with convex hull (CH) and side‐view digital area (SA; r ≤ 0.76 and r ≤ 0.68 , respectively). In the control condition, a more negative correlation was obtained between Normalized Difference Vegetation Index (NDVI) with green leaf index (GLI) and top area (TA; r = −0.74 and −0.63, respectively). Also, the two architectural features solidity (SOL) and convex hull (CH) had a strong negative correlation ( r = −0.65). Also under drought stress, solidity (SOL) and convex hull (CH) had a significant negative correlation with the same tendency as the control condition ( r = −0.69). The more negative correlations were observed in dry conditions between some physiological traits green leaf index (GLI) with Normalized Difference Vegetation Index (NDVI), normalized pigment chlorophyll ratio index (NPCI), and plant senescence reflectance index (PSRI; r = −0.82, −0.72, and −0.64, respectively). 3.3. Principal Component Analysis (PCA) The PCA biplot illustrates patterns of trait variation and associations between control and drought conditions across various traits, with the first two PCA axes explaining approximately 50% of the total variation (Figure 4 ). The PC1 axis, accounting for almost 35% of the total variance, clearly separates the control and drought conditions, highlighting substantial overall differences. Factors such as PSRI, NDVI, and TA are positioned near to the two architectural traits PH and CH. All biomass‐related traits (except TA), along with certain architectural traits (such as SLA, LAR, and TLA), as well as NDVI, were grouped together. In contrast, traits like PhE and Sol showed distinct separation, indicating significant differences in their behaviour. FIGURE 4. Open in a new tab PCA biplot showing separation of plant samples under control and drought conditions. Blue circles (•): Control condition samples and Red triangles (▲): Drought condition samples. Axes: X‐axis: PC1 (Principal Component 1)—explains 34.6% of total variance and Y‐axis: PC2 (Principal Component 2)—explains 14.5% of total variance. Overlapping Areas: Blue shaded area: Distribution range of control samples, Red shaded area: Distribution range of drought samples, and overlap area: Where control and drought conditions share similar characteristics. Arrows: Black arrows showing trait loadings/directions. The length and direction of these arrows indicate how strongly and in what way each trait contributes to the separation between control and drought conditions. 3.4. The Performance of Different Species of Pisum on Selected Agronomic Under Drought Conditions The experiment included a diverse collection of Pisum species, including Pisum fulvum , P. abyssinicum , and several subspecies of P. sativum (named jomardii , arvense , sativum , humile , elatius , and the Indian ecotype of P. sativum ). Given this genetic diversity, it is valuable to evaluate the performance of these species under drought conditions, focusing on their tolerance and response to important agronomic traits. In this regard, the species‐specific effect on the percentage reduction of dry weight (DW) and plant height (PH), both measured manually under drought conditions, was compared. In addition, water use efficiency (WUE) and PSII Photochemical efficiency (PhE), obtained by image‐based methods, were evaluated. This analysis will help to understand the role of interspecific variation in drought tolerance (or sensitivity) as well as resource utilization under stress conditions in future studies. Pisum sativum subsp. elatius , Pisum fulvum and P. sativum subsp. humile demonstrated a smaller decrease in dry weight under drought conditions compared to the control condition (Figure 5a ). The greatest reduction in dry weight under drought conditions was observed in P. sativum “Indian ecotype”. FIGURE 5. Open in a new tab Effect of drought on different plant species. (A) the percentage reduction of dry weight (DW), (B) the percentage reduction of plant height (PH), (C) water use efficiency (WUE) and (D) PSII Photochemical efficiency (PHE), under drought condition, the script calculates the average across all species. The red dashed line shows the overall average of the trait across all species. A similar trend with dry weight was observed for plant height (Figure 5b ), although in this case P. fulvum showed the smallest decrease, followed by P. humile and then P. elatius , while the most significant decrease in plant height under drought condition occurred in P. sativum subsp. Sativum (Figure 5b ). In terms of water use efficiency (WUE), P. sativum “Indian ecotype”, P. abyssinicum and P. sativum subsp. sativum showed the highest performance, respectively, while P. fulvum and P. sativum subsp. elatius showed the lowest water use efficiency under drought conditions (Figure 5c ). In terms of PSII photochemical efficiency, P. sativum subsp. elatius , P. abyssinicum , P. sativum subsp. humile , and P. fulvum (in descending order) showed the best performance (Figure 5d ). On the other hand, P. sativum subsp. arvense and P. sativum subsp. jomardii showed the lowest PSII photochemical efficiency (PhE) under drought conditions, which means a reduced capacity to maintain photosynthetic activity under water limitation conditions. 4. Discussion The expression of traits related to pea growth and stress tolerance was investigated during the vegetative period. These traits were recorded using two different imaging cabinets that detect visible (colour), near‐infrared, fluorescence, and chlorophyll fluorescence. In this study, we focused on traits that are highly informative, showing effects of accessions and conditions, and biologically meaningful, which means they can serve as a proxy for agronomically important traits. Colour (RBB) imaging can be applied not only to evaluate the growth and other biomass‐related features of plants, but also to assess their health and physiological conditions (Kior et al. 2024 ). Fluorescence imaging also indicates chlorophyll signalling and can serve as an instrument for detecting stress symptoms (Janka et al. 2018 ; Elangovan et al. 2023 ). In this study, both image‐based features and manually measured attributes were used to assess pea plant traits. It is important to note that all image‐based data were recorded at the final measurement point, aligning with the manual measurements taken at the end of the experiment to ensure consistency. The study aimed to comprehensively evaluate the effects of drought stress on pea plants by examining three key groups of traits: biomass‐related, architectural, and physiological characteristics. Both manual and image‐based measurements provided valuable insights, with each offering complementary data that are equally integral to understanding the plants' overall physiological response to drought. As expected, drought stress induces significant alterations in plant growth and physiological traits, consistent with prior research in legumes and other crop species (Castillejo et al. 2016 ; Soureshjani et al. 2019 ; Godoy Androcioli et al. 2020 ). In particular, biomass‐related traits (except TA) were more sensitive to applied drought stress among the studied traits. The negative SI in LWR may be related to reducing transpiration and water loss under drought conditions (Ahmad et al. 2018 ). Additionally, increasing Sol as an architectural trait suggests potential adaptive responses, such as maintaining plant density under drought stress (Honda et al. 2019 ). PhE slightly increased under drought, suggesting a relatively stable response under water limitation. Several other traits also showed limited variation (SI < 25%; Table 1 ), highlighting their relative stability as reliable indicators of drought tolerance for early screening in breeding programs. LAR (1.28%) and NDVI (2.86%) as architectural and physiological traits, respectively, may provide useful information to deal with drought effects. Analysis of variance component revealed genetic diversity in the studied population and the significant influence of environmental factors on plant phenotypes, highlighting the role of genotype × environment interactions in determining plant traits. Similar observations have been reported by Mansouri et al. ( 2018 ) and Xu et al. ( 2020 ). Trait heritability is considered when a new trait is proposed for use in a breeding program to improve crops. Broad‐sense heritability ( h 2 ) represents the proportion of total phenotypic variance attributable to genetic factors (Schmidt et al. 2019 ). In the current study, several architectural traits exhibited slightly higher heritabilities (around 0.60 and greater), while physiological traits (except WUE) and biomass‐related traits showed lower values ( ≤ 56 and ≤ 36 , respectively). Similar reports indicated higher heritability of morphological traits than biomass traits in maize and wheat under drought stress, which may be related to the stronger environmental effects on biomass accumulation (Dodig et al. 2019 ; Khadka et al. 2020 ). Environmental conditions accounted for more than 50% of the variation in several biomass‐related traits, including FW, DW, and RWC. In general, heritability estimates of image‐derived data were mostly higher than for manual data, indicating the potential utility of high‐throughput phenotyping for studying plant growth and genetic diversity (Duc et al. 2023 ). The relatively high heritability estimate for the WUE (0.87) indicates significant genetic variation among the studied pea accessions and suggests that this trait may help detect variation in response to suboptimal soil moisture levels. We hypothesize that accessions with higher WUE may have lower biomass or more efficient root systems under water‐deficit conditions; however, further investigations are needed to clarify these relationships and evaluate their potential use in pea breeding programs. Similarly, the high heritability of Sol (0.68) in the dry state indicates physiological and structural adjustments. Compact growth forms, characterized by reduced shoot elongation, have been reported as a water‐saving strategy in various crops, including wheat and maize (Khatun et al. 2021 ; Mohi‐Ud‐Din et al. 2021 ). These observations are consistent with the concept that several physiological and morphological traits may respond in a coordinated manner under drought conditions (Zia et al. 2021 ). Similar findings in other legume crops (such as chickpeas and wheat) indicate that multi‐trait interactions become more pronounced under drought stress, reinforcing the importance of studying such integrated responses (Rani et al. 2020 ; Mohi‐Ud‐Din et al. 2024 ). Phenotypic correlations of manually measured traits and automated image‐derived traits can be provided for reliable estimates from a high‐throughput phenotyping approach (Dodig et al. 2021 ). The moderate positive relationship between DB (obtained from images) and FW and DW (measured manually) observed in the present study was somewhat lower under drought conditions compared with previously reported data in soybean and maize (Dodig et al. 2019 ; Ranđelović et al. 2023 ). Early stages of growth usually involve simpler plant structures, which may result in a stronger relationship between estimated digital biomass and actual plant weight. In later stages, leaf overlap, complex branching, or stem elongation reduce the accuracy of biomass measurements and the correlation (Olas et al. 2020 ). Increased correlation between traits under drought conditions indicates a coordinated stress response (Ambavaram et al. 2014 ). However, in the present study, drought conditions were associated with a stronger correlation between the image‐derived CH and manually measured PH. Under drought conditions, plants typically exhibit a more compact and upright growth habit, leading to a stronger correlation between the projected canopy area and manually measured PH (Joshi et al. 2021 ). Reduced leaf expansion and canopy spread under drought conditions may lead to plant architectures in which height and canopy area vary more closely (Tolley et al. 2020 ). The negative correlation obtained between GLI and other pigment indices (NDVI, NPCI, and PSRI), particularly under drought conditions, may be related to differences in their spectral sensitivities and the specific physiological characteristics of the plant they capture (Roman and Ursu 2016 ). GLI index is derived from the visible spectrum and primarily reflects canopy greenness, while other indices respond strongly to internal leaf structure and carotenoid accumulation (Giovos et al. 2021 ; Coswosk et al. 2024 ). Similar results have been reported regarding the sensitivity of GLI under drought stress compared to other pigment indices in common beans (Javornik et al. 2023 ). PCA is a statistical method used to reduce the dimensionality of variables and summarize patterns within highly correlated datasets (Gewers et al. 2021 ). Two‐dimensional PCA biplots help visualize patterns of trait variation and relationships between traits under different environmental conditions. PCA biplot analysis has been widely applied to screen the effects of drought on various legumes (Mohi‐Ud‐Din et al. 2021 ; Balko et al. 2023 ; Mohanlal et al. 2023 ; Basavaraj et al. 2025 ). In the present study, the complex distribution of accessions along PC1 and PC2 highlights significant variation within the current population, which can be utilized to identify drought‐tolerant lines of peas for breeding programs. In line with the correlation results, GLI did not cluster with other traits in the PCA analysis, indicating a distinct pattern of variation under the studied conditions. This pattern suggests that GLI may be relatively sensitive to drought stress and could provide useful information for the early detection of drought stress symptoms. Consistent with the SI results, PhE and Sol clustered together in the PCA biplot, indicating that these traits tend to change together and show slightly higher values under drought conditions. The association indicates that more compact canopies or structurally dense leaves help maintain higher photosynthetic performance, especially under stress conditions (Vico et al. 2023 ). Additionally, variation in Fv/fm under drought conditions may be associated with differences in PSII photochemical efficiency among accessions (Murchie and Lawson 2013 ). These results suggest that pea accessions with higher Sol and PhE may adopt a conservative water‐use strategy and maintain their efficiency under stress. The strong clustering of NDVI with biomass‐related traits (except TA) and WUE indicates a close relationship between these features. Similar positive relationship between NDVI and biomass characteristics have also been observed in other legumes such as common beans and soybeans (Barboza et al. 2023 ; Farias et al. 2023 ). However, the short WUE arrow in the PCA biplot suggests that WUE is relatively stable across accessions. These observations are particularly useful in high‐throughput phenotyping, where NDVI can serve as a rapid representative for estimating biomass accumulation as well as for discovering drought‐tolerant genotypes. The clustering of CH, PH, NPCI, PSRI, and TA in the PCA biplot indicates these traits varied together along the same axis of the analysis, reflecting shared variation among plant size and spectral traits. This could mean that growth‐oriented and taller pea accessions are more vulnerable to environmental stresses such as drought compared to smaller plants as were reported in other legumes (Wang et al. 2021 ; Mansour et al. 2021 ). This foundational work has laid the groundwork for subsequent experiments and trials in which these findings will be validated at a larger scale (COUSIN project, www.cousinproject.eu ). Also, in this study, significant variation was observed among Pisum species and subspecies in response to drought stress, indicating extensive genetic diversity within this genus. Notably, the “Indian ecotype” of P. sativum showed the highest dry weight loss under drought conditions (Figure 5a ), indicating a high sensitivity of biomass to water limitation. Similarly, P. sativum subsp. sativum experienced the highest plant height loss (Figure 5b ), further supporting the observation that cultivated species have limited adaptive capacity under water stress conditions. These results are consistent with other research, which shows that domesticated genotypes often lose drought‐adapted traits during the breeding process, due to a trade‐off between yield potential and stress tolerance (Annicchiarico et al. 2019 ). On the other hand, wild relatives such as P. fulvum and P. elatius maintained more stable growth parameters under drought conditions, possibly due to deeper root systems or conservative growth strategies that evolved under natural selection in arid environments (Smýkal et al. 2018 ). Another reason may be their inherently smaller stature and slower growth rate, which limit water demand and reduce the apparent impact of drought stress on morphological traits. In other words, the slower, more intensive growth rate, as well as the conservative resource‐use strategy in wild relatives, could provide drought avoidance by reducing transpiration and structural investment. Such performances have been usually reported in some wild legumes adapted to stress conditions (Smýkal et al. 2018 ; Berger et al. 2017 ) and could elucidate the relative resilience of these wild species under drought stress. Regarding physiological traits, water use efficiency (WUE) and PSII photochemical efficiency (PhE) provided additional vision into the response of different species to drought. P. sativum ‘Indian ecotype’, P. abyssinicum and P. sativum subsp. sativum showed relatively high water use efficiency (Figure 5c ), indicating their ability to maintain biomass accumulation with lower water consumption, an important feature for productivity under drought conditions (Tardieu et al. 2018 ). In contrast, the species P. fulvum and P. elatius , although showing low water use efficiency, still exhibited good performance in PSII photochemical efficiency, especially P. elatius , P. abyssinicum and P. humile , indicating that the high‐efficiency photosynthetic apparatus may partially compensate for the low water use characteristics (Figure 5c,d ). The ability of these species to maintain photosynthetic activity under drought stress conditions probably helps their tolerance, as photosynthesis is one of the first physiological characters to be affected by water shortage (Qiao et al. 2024 ). It is noteworthy that P. arvense and P. jomardii showed the lowest photosynthetic efficiency, which may indicate stomatal closure or photosynthetic pigment degradation under stress (Stefanov et al. 2023 ). 5. Conclusions This study provides a comprehensive assessment of trait performance across a pea collection under drought conditions, identifying key indicators of drought tolerance. Furthermore, the results of this study show that high‐throughput phenotyping could provide opportunities to discover important drought‐tolerance traits in pea. For greater certainty, the outputs obtained by the images were compared to manual measurements. The notable reduction observed in biomass accumulation and leaf area traits, in contrast to the moderate flexibility in photosynthetic indices, reveals fundamental trade‐offs between growth factors and survival strategies. The high SI values obtained in almost all biomass‐related traits indicate their significant sensitivity to drought stress; therefore, these traits can be considered as priority indicators in stress research. On the other hand, the stability of traits such as PSII photochemical efficiency (PhE) and solidity (SOL) under drought conditions indicates their advantages for selecting the most suitable pea lines in breeding programs. Additionally, PCA analysis highlights the crucial role of specific structural and physiological traits in plant adaptation to water stress. Overall, this study provided comprehensive results for farmers, researchers, and policymakers to identify, predict, and manage the multiple impacts of drought stress on agriculture and natural ecosystems. Continuous monitoring and analysis of these important factors under different climatic conditions will help develop effective strategies for better adaptation to climate and drought challenges. Additionally, this study reveals significant variation in drought response among diverse Pisum species and subspecies, demonstrating their distinct evolutionary histories and adaptive strategies. Some wild relatives, such as P. elatius and P. fulvum , displayed fairly stable morphological traits under drought stress, likely due to their shorter stature and conservative growth rates. Conversely, some cultivated species, such as P. sativum “Indian ecotype” and subspecies sativum , showed more reductions in biomass and height under drought conditions, while exhibiting superior water use efficiency, indicating the potential for drought adaptation through efficient water use. Moreover, species with higher PSII photochemical efficiency, including P. elatius, P. abyssinicum , and P. humile , demonstrated their ability to maintain physiological function under drought stress. These results emphasize the value of integrating morphological and physiological traits for selecting drought‐tolerant pea genotypes and highlight the unrecognized potential of wild germplasm in breeding programs aimed at improving climate resistance in pea and other legumes. Author Contributions M.W.V. and C.S.S. conceived and designed the research. M.B. and R.Z. performed the experiment. M.B. and R.Z. analyzed the data. M.W.V., C.S.S. and D.R. guided the research. M.B. wrote the original draft. M.W.V., C.S.S., D.R. and R.Z. revised the manuscript. Funding This work was supported by Horizon Europe, 101135314; Spanish Ministry of Science and Innovation, MCIN/AEI/10.13039/501100011033; FCT‐Fundação para a Ciência e Tecnologia (Portugal), 2021.08330.BD, 2023.15056.TENURE.039, 2023.15056.TENURE.060. Conflicts of Interest The authors declare no conflicts of interest. Supporting information Table S1: Summary of the investigated phenotypic traits in this study. Table S2: Sum of squares measures total variation from the mean. Mean squareis sum of squares divided by degrees of freedom for image‐based traits of 180 pea accessions grown under control and drought stress conditions. F ratio tests factor significance. p ‐value (Prob > F) indicates result probability. Table S3: Standard deviation (σ) and coefficient of variation (CV) for image‐based and manually measured traits of 180 pea accessions grown under Control and drought stress. Table S4: REML variance components (± standard error of estimates) for random model. Table S5: Matrix for correlation coefficients (r) showing the simple linear relationship among all mesured traits in control condition. Table S6: Matrix for correlation coefficients (r) showing the simple linear relationship among all mesured traits in drought condition. Figure S1: Boxplots summarizing the phenotypic distribution within each condition (control and Drought stress) for all measured traits. PPL-178-e70863-s001.pdf (676.4KB, pdf) Acknowledgments This research was supported by the European Union’s Horizon Research and Innovation Programme through the project “Crop wild relatives utilisation and conservation for sustainable agriculture” (COUSIN), Grant Agreement number 101135314. This work was also funded by national funds through FCT—Fundação para a Ciência e a Tecnologia, I.P., through PhD scholarship 2021.08330.BD, contracts ref. 2023.15056.TENURE.060 (granted to M.W.V.) and 2023‐15056.TENURE.039 (granted to C.S.S.) and under UID/50016/2025 and LA/P/0076/2020 (https://doi.org/10.54499/LA/P/0076/2020). D.R. acknowledges the financial support from Spanish project PID2023‐146215OB‐I00 (MCIN/AEI/10.13039/501100011033). We thank the Netherlands Plant Eco‐phenotyping Centre (NPEC) for their help with carrying out this experiment. Open access publication funding provided by FCT (b‐on). Bagheri, M. , van de Zedde R., Rubiales D., Santos C. S., and Vasconcelos M. W.. 2026. “High‐Throughput Phenotyping for Revealing Key Morpho‐Physiological Traits for Drought Tolerance in Pea ( Pisum sativum and Wild Relatives).” Physiologia Plantarum 178, no. 2: e70863. 10.1111/ppl.70863. Handling Editor: P. Carillo Data Availability Statement All data in this study are described in the main text and its Supporting Information . For more data related to this paper, requests should be made to the authors. References Abdel‐Hamid, A. M. , and Salem K. F.. 2021. “Breeding Strategies of Garden Pea ( Pisum sativum L.).” In Advances in Plant Breeding Strategies: Vegetable Crops, edited by Al‐Khayri J. M., Jain S. M., and Johnson D. V., vol. 10, 331–377. Springer. [ Google Scholar ] Ahmad, Z. , Waraich E. A., Akhtar S., et al. 2018. “Physiological Responses of Wheat to Drought Stress and Its Mitigation Approaches.” Acta Physiologiae Plantarum 40: 1–13. [ Google Scholar ] Ambavaram, M. M. R. , Basu S., Krishnan A., et al. 2014. “Coordinated Regulation of Photosynthesis in Rice Increases Yield and Tolerance to Environmental Stress.” Nature Communications 5, no. 1: 5302. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Annicchiarico, P. , Nazzicari N., Pecetti L., Romani M., and Russi L.. 2019. “Pea Genomic Selection for Italian Environments.” BMC Genomics 20, no. 1: 603. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Asaari, M. S. M. , Mertens S., Dhondt S., Inzé D., Wuyts N., and Scheunders P.. 2019. “Analysis of Hyperspectral Images for Detection of Drought Stress and Recovery in Maize Plants in a High‐Throughput Phenotyping Platform.” Computers and Electronics in Agriculture 162: 749–758. [ Google Scholar ] Bagheri, M. , Santos C. S., Rubiales D., and Vasconcelos M. W.. 2023. “Challenges in Pea Breeding for Tolerance to Drought: Status and Prospects.” Annals of Applied Biology 183, no. 2: 108–120. [ Google Scholar ] Balko, C. , Torres A. M., and Gutierrez N.. 2023. “Variability in Drought Stress Response in a Panel of 100 Faba Bean Genotypes.” Frontiers in Plant Science 14: 1236147. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Barboza, T. O. C. , Ardigueri M., Souza G. F. C., Ferraz M. A. J., Gaudencio J. R. F., and Santos A. F. D.. 2023. “Performance of Vegetation Indices to Estimate Green Biomass Accumulation in Common Bean.” Agricultural Engineering 5, no. 2: 840–854. [ Google Scholar ] Basavaraj, P. S. , Rane J., Jangid K. K., et al. 2025. “Index‐Based Selection of Chickpea ( Cicer arietinum L.) Genotypes for Enhanced Drought Tolerance.” Scientific Reports 15, no. 1: 8282. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Belachew, K. Y. , Nagel K. A., Fiorani F., and Stoddard F. L.. 2018. “Use of GROWSCREEN‐Rhizo Boxes Allowed Detection of Useful Differences in Root Response to Water Deficit in Faba Bean ( Vicia faba L.).” Advances in Legume Science and Practice 138: 135–140. [ Google Scholar ] Berger, J. D. , Shrestha D., and Ludwig C.. 2017. “Reproductive Strategies in Mediterranean Legumes: Trade‐Offs Between Phenology, Seed Size and Vigor Within and Between Wild and Domesticated Lupinus Species Collected Along Aridity Gradients.” Frontiers in Plant Science 8: 548. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Bhat, J. A. , Deshmukh R., Zhao T., et al. 2020. “Harnessing High‐Throughput Phenotyping and Genotyping for Enhanced Drought Tolerance in Crop Plants.” Journal of Biotechnology 324: 248–260. [ DOI ] [ PubMed ] [ Google Scholar ] Bhat, J. A. , and Yu D.. 2021. “High‐Throughput NGS‐Based Genotyping and Phenotyping: Role in Genomics‐Assisted Breeding for Soybean Improvement.” Legume Science 3, no. 3: e81. [ Google Scholar ] Castillejo, M. A. , Iglesias‐García R., Wienkoop S., and Rubiales D.. 2016. “Label‐Free Quantitative Proteomic Analysis of Tolerance to Drought in Pisum sativum .” Proteomics 16: 2776–2782. [ DOI ] [ PubMed ] [ Google Scholar ] Cobb, J. N. , DeClerck G., Greenberg A., Clark R., and McCouch S.. 2013. “Next‐Generation Phenotyping: Requirements and Strategies for Enhancing Our Understanding of Genotype–Phenotype Relationships and Its Relevance to Crop Improvement.” Theoretical and Applied Genetics 126: 867–887. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Correia, P. M. , Cairo Westergaard J., Bernardes da Silva A., Roitsch T., Carmo‐Silva E., and Marques da Silva J.. 2022. “High‐Throughput Phenotyping of Physiological Traits for Wheat Resilience to High Temperature and Drought Stress.” Journal of Experimental Botany 73, no. 15: 5235–5251. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Coswosk, G. G. , Gonçalves V. M. L., de Lima V. J., et al. 2024. “Utilizing Visible Band Vegetation Indices From Unmanned Aerial Vehicle Images for Maize Phenotyping.” Remote Sensing 16, no. 16: 3015. [ Google Scholar ] Dar, Z. A. , Dar S. A., Khan J. A., et al. 2021. “Identification for Surrogate Drought Tolerance in Maize Inbred Lines Utilizing High‐Throughput Phenomics Approach.” PLoS One 16, no. 7: e0254318. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [ Retracted ] Dodig, D. , Božinović S., Nikolić A., et al. 2019. “Image‐Derived Traits Related to Mid‐Season Growth Performance of Maize Under Nitrogen and Water Stress.” Frontiers in Plant Science 10: 814. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Dodig, D. , Božinović S., Nikolić A., et al. 2021. “Dynamics of Maize Vegetative Growth and Drought Adaptability Using Image‐Based Phenotyping Under Controlled Conditions.” Frontiers in Plant Science 12: 652116. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Duc, N. T. , Ramlal A., Rajendran A., et al. 2023. “Image‐Based Phenotyping of Seed Architectural Traits and Prediction of Seed Weight Using Machine Learning Models in Soybean.” Frontiers in Plant Science 14: 1206357. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Elangovan, A. , Duc N. T., Raju D., et al. 2023. “Imaging Sensor‐Based High‐Throughput Measurement of Biomass Using Machine Learning Models in Rice.” Agriculture 13, no. 4: 852. [ Google Scholar ] Espinosa‐Tasón, J. , Berbel J., Gutiérrez‐Martín C., and Musolino D. A.. 2022. “Socioeconomic Impact of 2005–2008 Drought in Andalusian Agriculture.” Science of the Total Environment 826: 154148. [ DOI ] [ PubMed ] [ Google Scholar ] FAOSTAT . 2024. “Crops and Livestock Products (Food and Agriculture Organization (FAO)).” Farias, G. D. , Bremm C., Bredemeier C., et al. 2023. “Normalized Difference Vegetation Index (NDVI) for Soybean Biomass and Nutrient Uptake Estimation in Response to Production Systems and Fertilization Strategies.” Frontiers in Sustainable Food Systems 6: 959681. [ Google Scholar ] Farooqi, Z. U. R. , Ayub M. A., Rehman M. Z. u., et al. 2020. “Regulation of Drought Stress in Plants.” In Plant life under changing environment, 77–104. Academic Press. [ Google Scholar ] Gewers, F. L. , Ferreira G. R., Arruda H. F. D., et al. 2021. “Principal Component Analysis: A Natural Approach to Data Exploration.” ACM Computing Surveys (CSUR) 54, no. 4: 1–34. [ Google Scholar ] Giovos, R. , Tassopoulos D., Kalivas D., Lougkos N., and Priovolou A.. 2021. “Remote Sensing Vegetation Indices in Viticulture: A Critical Review.” Agriculture 11, no. 5: 457. [ Google Scholar ] Gitelson, A. A. , and Merzlyak M. N.. 1998. “Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves.” Advances in Space Research 22, no. 5: 689–692. [ Google Scholar ] Godoy Androcioli, L. , Mariani Zeffa D., Soares Alves D., Pires Tomaz J., and Moda‐Cirino V.. 2020. “Effect of Water Deficit on Morphoagronomic and Physiological Traits of Common Bean Genotypes With Contrasting Drought Tolerance.” Water 12, no. 1: 217. [ Google Scholar ] Gondalia, N. , Vashi R., Barot V., et al. 2022. “Genomic Designing for Abiotic Stress Tolerance in Pea ( Pisum sativum L.).” In Genomic Designing for Abiotic Stress Resistant Pulse Crops, 45–113. Springer International Publishing. [ Google Scholar ] Holland, J. B. , Nyquist W. E., Cervantes‐Martínez C. T., and Janick J.. 2003. “Estimating and Interpreting Heritability for Plant Breeding: An Update.” Plant Breeding Reviews 22: 9–112. [ Google Scholar ] Honda, E. A. , Pilon N. A. L., and Durigan G.. 2019. “The Relationship Between Plant Density and Survival to Water Stress in Seedlings of a Legume Tree.” Acta Botânica Brasílica 33, no. 3: 602–606. [ Google Scholar ] Iglesias‐García, R. , Prats E., Fondevilla S., Satovic Z., and Rubiales D.. 2015. “Quantitative Trait Loci Associated to Drought Adaptation in Pea ( Pisum sativum L.).” Plant Molecular Biology Reporter 33: 1768–1778. [ Google Scholar ] Janka, E. , Körner O., Rosenqvist E., and Ottosen C. O.. 2018. “Simulation of PSII‐Operating Efficiency From Chlorophyll Fluorescence in Response to Light and Temperature in Chrysanthemum ( Dendranthema grandiflora ) Using a Multilayer Leaf Model.” Photosynthetica 56: 633–640. [ Google Scholar ] Jaramillo Roman, V. , van de Zedde R., Peller J., Visser R. G., van der Linden C. G., and van Loo E. N.. 2021. “High‐Resolution Analysis of Growth and Transpiration of Quinoa Under Saline Conditions.” Frontiers in Plant Science 12: 634311. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Javornik, T. , Carović‐Stanko K., Gunjača J., Vidak M., and Lazarević B.. 2023. “Monitoring Drought Stress in Common Bean Using Chlorophyll Fluorescence and Multispectral Imaging.” Plants 12, no. 6: 1386. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Joshi, S. , Thoday‐Kennedy E., Daetwyler H. D., Hayden M., Spangenberg G., and Kant S.. 2021. “High‐Throughput Phenotyping to Dissect Genotypic Differences in Safflower for Drought Tolerance.” PLoS One 16, no. 7: e0254908. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Juzoń, K. , Czyczyło‐Mysza I., Ostrowska A., Marcińska I., and Skrzypek E.. 2019. “Chlorophyll Fluorescence for Prediction of Yellow Lupin ( Lupinus luteus L.) and Pea ( Pisum sativum L.) Susceptibility to Drought.” Photosynthetica 57, no. 4: 950–959. [ Google Scholar ] Khadka, K. , Earl H. J., Raizada M. N., and Navabi A.. 2020. “A Physio‐Morphological Trait‐Based Approach for Breeding Drought Tolerant Wheat.” Frontiers in Plant Science 11: 715. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Khatun, M. , Sarkar S., Era F. M., et al. 2021. “Drought Stress in Grain Legumes: Effects, Tolerance Mechanisms and Management.” Agronomy 11, no. 12: 2374. [ Google Scholar ] Khuimphukhieo, I. , and da Silva J. A.. 2025. “Unmanned Aerial Systems (UAS)‐based Field High Throughput Phenotyping (HTP) as Plant Breeders' Toolbox: A Comprehensive Review.” Smart Agricultural Technology 100888: 100888. [ Google Scholar ] Kim, M. , Lee C., Hong S., Kim S. L., Baek J. H., and Kim K. H.. 2021. “High‐Throughput Phenotyping Methods for Breeding Drought‐Tolerant Crops.” International Journal of Molecular Sciences 22, no. 15: 8266. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Kior, A. , Yudina L., Zolin Y., Sukhov V., and Sukhova E.. 2024. “RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review.” Plants 13, no. 9: 1262. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Louhaichi, M. , Borman M. M., and Johnson D. E.. 2001. “Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat.” Geocarto International 16, no. 1: 65–70. [ Google Scholar ] Mansour, E. , Mahgoub H. A., Mahgoub S. A., et al. 2021. “Enhancement of Drought Tolerance in Diverse Vicia faba Cultivars by Inoculation With Plant Growth‐Promoting Rhizobacteria Under Newly Reclaimed Soil Conditions.” Scientific Reports 11, no. 1: 24142. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Mansouri, S. , Hajarolasvadi N., and Seyed Safavian H. R.. 2018. “Gene Expression and Biochemical Responses of Two Iranian Wheat ( Triticum aestivum L.) Cultivars Under Drought Stress.” Plant Physiology and Biochemistry 127: 134–143. [ Google Scholar ] Merzlyak, M. N. , Gitelson A. A., Chivkunova O. B., and Rakitin V. Y.. 1999. “Non‐Destructive Optical Detection of Pigment Changes During Leaf Senescence and Fruit Ripening.” Physiologia Plantarum 106, no. 1: 135–141. [ Google Scholar ] Mohanlal, V. A. , Saravanan K., and Sabesan T.. 2023. “Application of Principal Component Analysis (PCA) for Blackgram [ Vigna mungo (L.) Hepper] Germplasm Evaluation Under Normal and Water Stressed Conditions.” Legume Research 46, no. 9: 1134–1140. [ Google Scholar ] Mohi‐Ud‐Din, M. , Hossain M. A., Rohman M. M., et al. 2021. “Multivariate Analysis of Morpho‐Physiological Traits Reveals Differential Drought Tolerance Potential of Bread Wheat Genotypes at the Seedling Stage.” Plants 10, no. 5: 879. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Mohi‐Ud‐Din, M. , Hossain M. A., Rohman M. M., et al. 2024. “Multi‐Trait Index‐Based Selection of Drought Tolerant Wheat: Physiological and Biochemical Profiling.” Plants 14, no. 1: 35. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Murchie, E. H. , and Lawson T.. 2013. “Chlorophyll Fluorescence Analysis: A Guide to Good Practice and Understanding Some New Applications.” Journal of Experimental Botany 64, no. 13: 3983–3998. [ DOI ] [ PubMed ] [ Google Scholar ] Nadeem, M. , Li J., Yahya M., et al. 2019. “Research Progress and Perspective on Drought Stress in Legumes: A Review.” International Journal of Molecular Sciences 20, no. 10: 2541. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Olas, J. J. , Fichtner F., and Apelt F.. 2020. “All Roads Lead to Growth: Imaging‐Based and Biochemical Methods to Measure Plant Growth.” Journal of Experimental Botany 71, no. 1: 11–21. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Olesen, J. E. , Trnka M., Kersebaum K. C., et al. 2011. “Impacts and Adaptation of European Crop Production Systems to Climate Change.” European Journal of Agronomy 34, no. 2: 96–112. [ Google Scholar ] Padilla‐Chacón, D. , Martínez‐Barajas E., García‐Esteva A., Leal‐Delgado R., Kohashi‐Shibata J., and Peña‐Valdivia C. B.. 2017. “Biomass Remobilization in Two Common Bean ( Phaseolus vulgaris L.) Cultivars Under Water Restriction.” South African Journal of Botany 112: 79–88. [ Google Scholar ] Peñuelas, J. , Gamon J. A., Fredeen A. L., Merino J., and Field C. B.. 1994. “Reflectance Indices Associated With Physiological Changes in Nitrogen‐and Water‐Limited Sunflower Leaves.” Remote Sensing of Environment 48, no. 2: 135–146. [ Google Scholar ] Pinke, Z. , Decsi B., Demeter G., Kalicz P., Kern Z., and Acs T.. 2024. “Continental Lowlands Face Rising Crop Vulnerability: Structural Change in Regional Climate Sensitivity of Crop Yields, Hungary (Central and Eastern Europe), 1921–2010.” Regional Environmental Change 24, no. 1: 33. [ Google Scholar ] Qiao, M. , Hong C., Jiao Y., Hou S., and Gao H.. 2024. “Impacts of Drought on Photosynthesis in Major Food Crops and the Related Mechanisms of Plant Responses to Drought.” Plants 13, no. 13: 1808. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] R Core Team . 2022. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R‐project.org/ . [ Google Scholar ] Ranđelović, P. , Đorđević V., Miladinović J., Prodanović S., Ćeran M., and Vollmann J.. 2023. “High‐Throughput Phenotyping for Non‐Destructive Estimation of Soybean Fresh Biomass Using a Machine Learning Model and Temporal UAV Data.” Plant Methods 19, no. 1: 89. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Rani, A. , Devi P., Jha U. C., Sharma K. D., Siddique K. H., and Nayyar H.. 2020. “Developing Climate‐Resilient Chickpea Involving Physiological and Molecular Approaches With a Focus on Temperature and Drought Stresses.” Frontiers in Plant Science 10: 1759. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Roman, A. , and Ursu T.. 2016. “Multispectral Satellite Imagery and Airborne Laser Scanning Techniques for the Detection of Archaeological Vegetation Marks.” Landscape Archaeology on the Northern Frontier of the Roman Empire at Porolissum: An Interdisciplinary Research Project: 141–152. [ Google Scholar ] Saglam, A. , Saruhan N., Terzi R., and Kadioglu A.. 2011. “The Relations Between Antioxidant Enzymes and Chlorophyll Fluorescence Parameters in Common Bean Cultivars Differing in Sensitivity to Drought Stress.” Russian Journal of Plant Physiology 58, no. 1: 60–68. [ Google Scholar ] Santos, R. , Carvalho M., Rosa E., Carnide V., and Castro I.. 2020. “Root and Agro‐Morphological Traits Performance in Cowpea Under Drought Stress.” Agronomy 10, no. 10: 1604. [ Google Scholar ] Schmidt, P. , Hartung J., Rath J., and Piepho H. P.. 2019. “Estimating Broad‐Sense Heritability With Unbalanced Data From Agricultural Cultivar Trials.” Crop Science 59, no. 2: 525–536. [ Google Scholar ] Sheikh, M. , Iqra F., Ambreen H., Pravin K. A., Ikra M., and Chung Y. S.. 2024. “Integrating Artificial Intelligence and High‐Throughput Phenotyping for Crop Improvement.” Journal of Integrative Agriculture 23, no. 6: 1787–1802. [ Google Scholar ] Smýkal, P. , Trněný O., Brus J., et al. 2018. “Genetic Structure of Wild Pea ( Pisum sativum Subsp. Elatius ) Populations in the Northern Part of the Fertile Crescent Reflects Moderate Cross‐Pollination and Strong Effect of Geographic but Not Environmental Distance.” PLoS One 13, no. 3: e0194056. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Smýkal, P. , Varshney R. K., Singh V., et al. 2016. “From Mendel's Discovery on Pea to Today's Plant Genetics and Breeding: Commemorating the 150th Anniversary of the Reading of Mendel's Discovery.” Theoretical and Applied Genetics 129: 2267–2280. [ DOI ] [ PubMed ] [ Google Scholar ] Soureshjani, H. K. , Nezami A., Kafi M., and Tadayon M.. 2019. “Responses of Two Common Bean ( Phaseolus vulgaris L.) Genotypes to Deficit Irrigation.” Agricultural Water Management 213: 270–279. [ Google Scholar ] Stefanov, M. , Rashkov G., Borisova P., and Apostolova E.. 2023. “Sensitivity of the Photosynthetic Apparatus in Maize and Sorghum Under Different Drought Levels.” Plants 12, no. 9: 1863. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Tardieu, F. , Simonneau T., and Muller B.. 2018. “The Physiological Basis of Drought Tolerance in Crop Plants: A Scenario‐Dependent Probabilistic Approach.” Annual Review of Plant Biology 69: 733–759. [ DOI ] [ PubMed ] [ Google Scholar ] Thavarajah, D. , Lawrence T., Boatwright L., et al. 2023. “Organic Dry Pea ( Pisum sativum L.): A Sustainable Alternative Pulse‐Based Protein for Human Health.” PLoS One 18, no. 4: e0284380. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Tolley, S. , Yang Y., and Mohammadi M.. 2020. “High‐Throughput Phenotyping Identifies Plant Growth Differences Under Well‐Watered and Drought Treatments.” Journal of Integrative Agriculture 19, no. 10: 2429–2438. [ Google Scholar ] Trapp, J. J. , Urrea C. A., Zhou J., Khot L. R., Sankaran S., and Miklas P. N.. 2016. “Selective Phenotyping Traits Related to Multiple Stress and Drought Response in Dry Bean.” Crop Science 56, no. 4: 1460–1472. [ Google Scholar ] Ursem, R. , Tikunov Y., Bovy A., Van Berloo R., and Van Eeuwijk F.. 2008. “A Correlation Network Approach to Metabolic Data Analysis for Tomato Fruits.” Euphytica 161: 181–193. [ Google Scholar ] Vicente Serrano, S. , Domínguez‐Castro F., Murphy C., et al. 2020. “Long‐Term Variability and Trends in Meteorological Droughts in Western Europe (1851–2018).” International Journal of Climatology 41, no. S1: E690–E717. [ Google Scholar ] Vico, G. , Tang F. H. M., Brunsell N. A., Crews T. E., and Katul G. G.. 2023. “Photosynthetic Capacity, Canopy Size and Rooting Depth Mediate Response to Heat and Water Stress of Annual and Perennial Grain Crops.” Agricultural and Forest Meteorology 341: 109666. [ Google Scholar ] Vishal, M. K. , Tamboli D., Patil A., et al. 2020. “Image‐based phenotyping of diverse rice ( Oryza sativa L.) genotypes. arXiv preprint arXiv:2004.02498.” Wang, N. , Li Q., Liu X., et al. 2021. “Plant Size Plays an Important Role in Plant Responses to Low Water Availability and Defoliation in Two Woody Leguminosae Species.” Frontiers in Plant Science 12: 643143. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Xu, W. , Cui K., Xu A., Nie L., and Huang J.. 2020. “Drought Stress Condition Increases Root to Shoot Ratio via Alteration of Carbohydrate Partitioning and Enzymatic Activity in Rice Seedlings.” Acta Physiologiae Plantarum 42, no. 4: 1–13. [ Google Scholar ] Yang, W. , Feng H., Zhang X., et al. 2020. “Crop Phenomics and High‐Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives.” Molecular Plant 13, no. 2: 187–214. [ DOI ] [ PubMed ] [ Google Scholar ] Zia, R. , Nawaz M. S., Siddique M. J., Hakim S., and Imran A.. 2021. “Plant Survival Under Drought Stress: Implications, Adaptive Responses, and Integrated Rhizosphere Management Strategy for Stress Mitigation.” Microbiological Research 242: 126626. [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Table S1: Summary of the investigated phenotypic traits in this study. Table S2: Sum of squares measures total variation from the mean. Mean squareis sum of squares divided by degrees of freedom for image‐based traits of 180 pea accessions grown under control and drought stress conditions. F ratio tests factor significance. p ‐value (Prob > F) indicates result probability. Table S3: Standard deviation (σ) and coefficient of variation (CV) for image‐based and manually measured traits of 180 pea accessions grown under Control and drought stress. Table S4: REML variance components (± standard error of estimates) for random model. Table S5: Matrix for correlation coefficients (r) showing the simple linear relationship among all mesured traits in control condition. Table S6: Matrix for correlation coefficients (r) showing the simple linear relationship among all mesured traits in drought condition. Figure S1: Boxplots summarizing the phenotypic distribution within each condition (control and Drought stress) for all measured traits. PPL-178-e70863-s001.pdf (676.4KB, pdf) Data Availability Statement All data in this study are described in the main text and its Supporting Information . For more data related to this paper, requests should be made to the authors. 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