# Robustness of high-throughput prediction of leaf ecophysiological traits using near infrared spectroscopy and poro-fluorometry

Eva Coindre, Romain Boulord, Laurine Chir, Virgilio Freitas, Maxime Ryckewaert, Thomas Laisné, Virginie Bouckenooghe, Maëlle Lis, Llorenç Cabrera-Bosquet, Agnès Doligez, et al.

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## To cite this version:

Eva Coindre, Romain Boulord, Laurine Chir, Virgilio Freitas, Maxime Ryckewaert, et al. Robustness of high-throughput prediction of leaf ecophysiological traits using near infrared spectroscopy and poro-fluorometry. Plant Phenome Journal, 2026, 9 (1), pp.e70059. 10.1002/ppj.2.70059. hal-05524078

HAL Id: hal-05524078

https://hal.inrae.fr/hal-05524078v1

Submitted on 23 Feb 2026

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ORIGINAL ARTICLE

Special Section: Phenomic Prediction in Plant Breeding

# Robustness of high-throughput prediction of leaf ecophysiological traits using near infrared spectroscopy and poro-fluorometry

Eva Coindre<fnref n="1" />,<fnref n="2" />

Romain Boulord<fnref n="2" />

Laurine Chir<fnref n="2" />

Virgilio Freitas<fnref n="1" />,<fnref n="2" />

Maxime Ryckewaert<fnref n="1" />,<fnref n="3" />

Thomas Laisné<fnref n="2" />

Virginie Bouckenooghe<fnref n="4" />,<fnref n="5" />

Maëlle Lis<fnref n="2" />

Llorenç Cabrera-Bosquet<fnref n="2" />

Agnès Doligez<fnref n="1" />,<fnref n="5" />

Thierry Simonneau<fnref n="2" />

Benoît Pallas<fnref n="2" />

Aude Coupel-Ledru<fnref n="2" />

Vincent Segura<fnref n="1" />,<fnref n="5" />

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**Correspondence**

Aude Coupel-Ledru, LEPSE, University of Montpellier, INRAE, Institut Agro, Montpellier, France. Email: aude.coupel-ledru@inrae.fr

Vincent Segura, UMR AGAP Institut, University of Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France. Email: vincent segura@inrae.fr

Assigned to Associate Editor Jennifer Clarke.

**Funding information**

Agence Nationale de la Recherche, Grant/Award Numbers: ANR-11-INBS-0012, ANR-16-CONV-0004, ANR-19-CE20-0024; Région Occitanie Pyrénées-Méditerranée, Grant/Award Number: Défi clé Vinid'Occ

**Abstract**

Water scarcity is a major threat to crop production and quality. Improving drought tolerance through variety selection requires a deeper understanding of plant ecophysiological responses, but large-scale phenotyping remains a bottleneck. This study assessed the potential of high-throughput tools (spectroscopy and poro-fluorometry) to predict leaf morphological and ecophysiological traits in a grapevine diversity panel grown in pots under well-watered outdoor conditions and under three contrasting soil water treatments in a greenhouse. We found a certain complementarity between measuring devices. Spectrometers could accurately predict leaf mass per area, water content, and water quantity ($R^2$ > 0.58), while the poro-fluorometer was efficient for predicting net CO₂ assimilation ($R^2$ > 0.72), regardless of the water treatment. The prediction of leaf mass per area using spectrometers appeared to be quite robust across both outdoor and greenhouse experiments, while the prediction of water use efficiency was dependent on the water treatment, with much better predictions under moderate ($R^2$ > 0.73) than severe water deficit. Calibrated models were then applied to the full diversity panel using only high-throughput measurements to estimate trait values and their broad-sense heritability. Leaf mass per area, also measured directly, showed similar heritability whether based on observed or predicted

**Abbreviations:** DW, dry weight; FW, fresh weight; IRGA, infrared gas-exchange analyzer; iWUE, intrinsic water use efficiency; LMA, leaf mass per area; LOO, leave-one-out; NIRS, near-infrared spectroscopy; NRMSE, normalized root mean squared error; PLSR, partial least square regression; PPFD, photosynthetically photon flux density; RMSE, root mean squared error; WQ, water quantity; WUE, water use efficiency; WW, well-watered.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2026 The Author(s). *The Plant Phenome Journal* published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop Science Society of America.

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<footnote n="1">¹UMR AGAP Institut, University of Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote n="2">²LEPSE, University of Montpellier, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote n="3">³Inria, LIRMM, University of Montpellier, CNRS, Montpellier, France</footnote>

<footnote n="4">⁴Institut français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi, France</footnote>

<footnote n="5">⁵Geno-Vigne®, IFV-INRAE-Institut Agro, Montpellier, France</footnote>
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data. Heritability estimates for predicted traits reached up to 0.5. Overall, our findings support the use of spectroscopy and poro-fluorometry as reliable, nondestructive tools for high-throughput phenotyping, enabling genetic studies on drought-related traits in grapevine.

**Plain Language Summary**

Drought is a major challenge for crop production. To breed grapevines that can better tolerate dry conditions, it is crucial to evaluate how plants respond to water stress using quick phenotyping tools. In this study, we tested two fast, nondestructive tools, near-infrared spectroscopy and poro-fluorometry, on grapevines grown with different water levels. Spectroscopy was accurate for evaluating leaf thickness and water content, while poro-fluorometry was better at predicting photosynthesis. These tools were effective even across different growing environments. The results demonstrate that these methods can aid in estimating genetic variability in drought-related traits, facilitating the selection and improvement of drought-tolerant grapevines.

# 1 | INTRODUCTION

Over the last decades, climate change has become a main concern. Prediction expects significant temperature rises and changes in annual rainfall patterns (IPCC, 2023; Cherif et al., 2020; Van Leeuwen et al., 2024). In order to maintain crops in this context, there is a need to explore new combinations of genetic and management choices. Identifying the best performing solutions further requires high-throughput characterization of traits related to plant functioning in responses to the environment.

Gas exchange, leaf structure, and specific leaf biochemical components are among the traits most impacted by abiotic constraints such as soil water availability. The regulation of gas exchange at the leaf level by stomatal closure/opening plays a key role in the whole plant response to water stress. Indeed, a soil water deficit leads to a drop in water potential all along the plant that could damage its water transfer capacity and result in organ or plant death (Simonneau et al., 2017). To prevent such damage, plants can quickly close their stomata, reducing transpiration (*E*) (Damour et al., 2010) and maintaining their leaf water content (WC~f~) (Bhattacharya, 2021). However, this water-saving strategy also decreases net photosynthesis (*A*~n~) by decreasing CO~2~ uptake (Simonneau et al., 2017), and in turn biomass production. A trade-off between reducing *E* and maintaining *A*~n~ under water stress requires considering water use efficiency (WUE), that is, the biomass produced per unit of water consumed. At the leaf level, it can be measured by the instantaneous WUE, calculated as the ratio between *A*~n~ and *E*, and the intrinsic water use efficiency (iWUE), calculated as the ratio between *A*~n~ and the stomatal conductance for water vapor (*g*~sw~). The latter provides

an estimate less dependent on air vapor pressure deficit than the former (Coupel-Ledru, 2021).

In grapevine, genotypic variability has already been reported for *A*~n~, *g*~sw~, iWUE, and leaf water potential (Ψ) (Bota et al., 2001; Prieto et al., 2010; Tomás et al., 2014). However, this variability was observed for a limited number of cultivars and/or in a limited number of growing conditions, usually well-watered (WW) conditions. High-throughput assessment of these ecophysiological traits is limited by the time required for measurements using leaf gas analyzers or pressure chambers. Studying large populations with a wide range of genetic diversity under various conditions is crucial to identify the best performing cultivars. It can also enable the identification of genetic regions associated with these traits and their responses to contrasted environmental conditions (Coupel-Ledru et al., 2014; Trenti et al., 2021). A main challenge is thus to investigate the use of alternative high-throughput methods as proxies to accurately predict traits of interest on large panels of genotypes under various water availability treatments and under different growing conditions (Xiao et al., 2022).

Fluorometer devices, providing chlorophyll fluorescence parameters related to photosystem II efficiency and electron transport rate, have been proposed as low-cost and rapid tools to estimate *A*~n~ (Han et al., 2022; Murchie & Lawson, 2013). Chlorophyll fluorescence measurements were also reported to evaluate the variability of photosynthetic performance among a population of hybrids in barley (Fernández-Calleja et al., 2020). Moreover, previous studies have calculated a semi-empirical index that combines fluorescence parameters and leaf temperature to estimate net photosynthesis in fruit trees (Losciale et al., 2015). This indicator was then used to
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evaluate the photosynthesis activity of a core collection of apples in response to water deficit (Coupel-Ledru et al., 2019).

Near-infrared spectroscopy (NIRS) is another high-throughput method commonly used to predict plant tissue composition (Cabrera-Bosquet et al., 2012; Foley et al., 1998; Petisco et al., 2006). Dedicated chemometrics methods, such as partial least square regression (PLSR) (Wold et al., 2001), are used to establish relationships between spectral absorbance or reflectance and ground-truth data obtained by low-throughput methods. NIRS has been reported in plants to predict numerous leaf compositional traits, including nitrogen (Ge et al., 2019) and carbon contents, lignin, cellulose (Petisco et al., 2006), protein, starch, total nonstructural carbohydrates (De Bei et al., 2017), and sugar concentrations (Ely et al., 2019; Van Wyngaard et al., 2021). NIRS has also been implemented to predict various morphological and ecophysiological traits on multiple species and on large populations of the same species. For instance, leaf mass per area (LMA), which is widely used in plant ecology as an indicator of plant functioning, has been predicted by NIRS on diverse species (Ely et al., 2019; Serbin et al., 2019). NIRS predictive models were also reported for LMA and $A_n$ on wheat and triticale germplasms (Silva-Perez et al., 2018) as well as on hybrids of maize (Cotrozzi et al., 2020). LMA, $A_n$, iWUE, WUE, $\Psi$, and WC~f~ were together predicted by NIRS on a diversity panel of maize (Ge et al., 2019), and $\Psi$ has also been predicted by NIRS on several cultivars of grapevine (De Bei et al., 2011; Rapaport et al., 2015). The relevance of NIRS to predict water-related traits was observed to be associated with light absorption by water at wavelength bands centered around 1400 and 1900 nm (Das et al., 2021; Workman & Weyer, 2012). To minimize the impact of water on spectra and consequently on the prediction accuracy of other traits, spectra can be collected on dried leaves (Masmola & Cho, 2019). Finally, not all spectrometers measure the same wavelength range. Therefore, the type of spectrometer must be rationalized according to the application (Barthès et al., 2019; Zhu et al., 2022).

When studying ecophysiological traits, the robustness of PLSR model predictions must be examined. On grapevine, predictive models with NIRS data have been built on few cultivars subjected to different soil water treatments (De Bei et al., 2011; Rapaport et al., 2015; Ryckewaert et al., 2022). These studies did not examine a wide range of genetic diversity, which is nevertheless required to perform genetic analyses. Moreover, PLSR models were calibrated and validated with datasets containing samples from all water treatments (De Bei et al., 2011; Ryckewaert et al., 2022). Only a few studies have evaluated the predictive ability of a model calibrated on a given growth condition (i.e., soil water treatment, for example) to predict the values of plants subjected to other growth conditions. If the predictive ability of PLSR models is robust across different environmental con

## Core Ideas

* High-throughput tools can predict key drought-response traits in grapevine.

* Near infrared spectroscopy and poro-fluorometry enable nondestructive phenotyping.

* Ecophysiological trait prediction is robust across contrasting environments.

* Predicted traits show accurate heritability, opening prospects for genetic studies.

ditions (with contrasting temperatures, wind, light, or soil water availability), phenotyping time could be reduced in new experiments by using such previously calibrated PLSR models, regardless of the growing conditions under which the measurements are performed. Another issue with the PLSR model is the risk of overfitting. To prevent this risk, the calibration model is usually built with a cross-validation (CV) procedure. However, even in a CV setting, the predictive ability of the model could still depend on the calibration and validation sets that could be randomly or non-randomly chosen (Filzmoser et al., 2009).

In this study, we evaluated the ability and complementarity of NIRS and poro-fluorometry data to predict leaf traits at high throughput on a large genetic variability. These two approaches were applied on a grapevine diversity panel (Nicolas et al., 2016), grown in pots both outdoors under non-limiting water availability and in a greenhouse with varying intensities of soil water deficit. The robustness of PLSR models was studied across experiments and water treatments. Heritabilities of traits either directly measured or estimated from high-throughput data were compared to investigate the ability of high-throughput phenotyping to assess the genotypic variability.

# 2 | MATERIALS AND METHODS

## 2.1 | Plant material and experimental design

We used a subset of 241 cultivars, selected based on the number of plants available, their health status, and the capacity of the phenotyping platform, from a panel of 279, previously designed to represent the worldwide genetic diversity of *Vitis vinifera* (Nicolas et al., 2016). We added five cultivars that are in the process of being registered in the French catalog, resulting in 246 studied cultivars. Twenty own-rooted plants were grown for each cultivar with ferti-irrigation from 2018 at the experimental vineyard of l'Institut Agro Montpellier (France) in individual 3-L pots containing a 30:70 (v/v)
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mixture of loamy soil and organic compost (Coupel-Ledru et al., 2024). In January 2020, the plants were transferred into larger, individual 9-L pots filled with the same substrate. A weather station was installed at the border of the vineyard. It recorded air temperature and relative humidity (HygroVue 10, Campbell Scientific Ltd), and photosynthetically active radiation (PAR) was computed from measurements of solar radiation obtained with a thermopile pyranometer (CS320, Campbell Scientific Ltd).

A first phenotyping season was conducted in July 2021, on the potted plants grown in the experimental vineyard under non-limiting, drip-irrigated conditions (outdoor experiment). Six plants out of the 20 available per cultivar were next retained to be part of a greenhouse experiment in 2022, where different watering treatments were set up (greenhouse experiment). This second experiment was conducted in the PhenoArch phenotyping platform (Cabrera-Bosquet et al., 2016) hosted at the Montpellier Plant Phenotyping Platforms (https://eng-lepse.montpellier.hub.inrae.fr/platforms-m3p/montpellier-plant-phenotyping-platforms-m3p), from May to August 2022. The whole panel (246 cultivars) was subjected to three different water treatments: (i) WW with no effect on leaf functioning, maintained at 1.7 g H₂O g⁻¹ of dry soil (soil water potential of -0.03 MPa), (ii) moderate water deficit (WD1) representative of onset of water deficit in field conditions that moderately affect leaf functioning, maintained at 1.05 g H₂O g⁻¹ of dry soil (soil water potential of -0.11 MPa), and (iii) severe water deficit (WD2) having a strong effect on leaf functioning to study the effect of advance water deficit closer to levels observed in field conditions, maintained at 0.5 g H₂O g⁻¹ of dry soil (soil water potential of -0.43 MPa), with two plants for each cultivar and each water treatment (1476 plants in total) (protocol for management of water treatment as in Coupel-Ledru et al. [2014]). For each cultivar, the six plants were grouped into two triplets (one triplet including one WW, one WD1, and one WD2 plant), and cultivar triplets were randomized within the phenotyping platform. Air temperature and relative humidity were monitored with a Rotronic HC2-SH (Rotronic AG). PAR was monitored with SKP 215 (Skye Instruments). These probes were distributed throughout the greenhouse at 10 different locations. Environmental variables were considered for June–July 2021 and June–July 2022, corresponding respectively to the measurement periods outdoors and in the greenhouse. The average daily PAR sum was 51 mol day⁻¹ in the outdoor experiment and 17 mol day⁻¹ in the greenhouse experiment. The average of the maximum daily values for the vapor pressure deficit, calculated using air temperature and relative humidity, was 2.20 and 1.58 kPa in outdoor and greenhouse experiments, respectively. On average daily, air temperature ranged from 18.7°C to 29.2°C in the outdoor experiment and from 19.0°C to 29.3°C in the greenhouse experiment.

## 2.2 | Leaf phenotyping

Leaf phenotyping was performed on two to three plants per cultivar in the outdoor experiment, while all the individuals were phenotyped in the greenhouse experiment once the water content in the pots had stabilized at their target values. For both experiments (outdoors and greenhouse), measurements were performed between 9:00 a.m. and 1:00 p.m. (UTC + 2). Leaves were chosen as fully expanded and well exposed to sunlight. They were usually located between the tenth and the 15th phytomer from the apex. Leaves were phenotyped between the 81 and 89 BBCH growth stages, where BBCH (Biologische Bundesanstalt, Bundessortenamt und CHemische Industries) refers to the phenological scale. Two kinds of measurements were performed on selected leaves: high- and low-throughput measurements. High-throughput measurements were carried out during five mornings on a total of 838 leaves in the outdoor experiment in July 2021 and during 24 mornings on a total of 1450 leaves in the greenhouse experiment in June and July 2022. Low-throughput measurements were performed only in the greenhouse experiment on one leaf per plant for a subset of 120 plants selected to be representative of the experimental design and thus covering the three watering conditions.

Multiple devices were used successively on the same leaf in situ, in a short time interval, for both high- and low-throughput phenotyping. The first device used was a handheld poro-fluorometer (Li600, Portable Photosynthesis System LI-600, Licor). It was used to measure the transpiration rate, *E* (mmol m⁻² s⁻¹), the stomatal conductance *g*<sub>sw</sub> (mol m⁻² s⁻¹), chlorophyll fluorescence parameters, and microclimate variables, that is, leaf vapor pressure deficit, atmospheric pressure, and ambient light (Table S1). Two measurements were taken at two different points on the same leaf and were averaged to obtain a single value per leaf. The second and third devices were micro-portable near-infrared spectrometers: MicroNIR (MicroNIR Onsite-W, VIAVI) and NeoSpectra (NeoSpectra Scanner, Si-Ware). The MicroNIR covers a spectral range from 908 to 1676 nm with 125 wavelengths, resulting in a spectral sampling interval of 6.2 nm. The NeoSpectra covers a spectral range from 1350 to 2500 nm with 257 wavelengths and a spectral sampling interval of 16 nm. The wavelength interval between 1350 and 1676 nm was common between both devices. For both devices, two spectra were collected per leaf on the lamina while avoiding the principal veins. These two spectra were respectively collected on each side of the leaf (abaxial and adaxial faces) and were then averaged to obtain a single spectrum per leaf. After these measurements, a 3-cm diameter disk was sampled from the leaf. In the greenhouse experiment, this disk was immediately weighed after sampling to obtain its fresh weight (FW, mg). For both outdoor and greenhouse experiments, the disk was oven-dried for 1 week at 60°C before being weighed to
get its dry weight (DW, mg). LMA (mg cm⁻²) was calculated as the ratio between DW and disk area. For disks of leaves collected in the greenhouse experiment only, leaf water content (WCf, mg H₂O mg⁻¹ FW) was also calculated as (FW – DW)/FW and the water quantity (WQ, mg H₂O per leaf disk) as (FW – DW). After weighing, dried disks were stored at 20°C. They were later used again to collect NIRS spectra using a laboratory spectrometer ASD (ASD LabSpec 4, Analytical Spectral Devices). The temperature was maintained at 20°C in the lab during measurements. Four spectra were taken using a reflectance probe (4 mm diameter) at different points of the dried disk before being averaged. The ASD covers a spectral range from 350 to 2500 nm with 2150 wavelengths and a sampling interval of 1 nm.

The low-throughput measurements were performed only in the greenhouse experiment during four mornings, chosen to be representative of the phenotyping season in terms of environmental conditions. The measurements started as described above for the high-throughput devices (i.e., Li600, MicroNir, and NeoSpectra) and were followed by a measure with an infrared gas-exchange analyzer (IRGA) equipped with a light-emitting diode light source (Li6800, Portable Photosynthesis System, Licor). This device allowed measuring net CO₂ assimilation (A_n, µmol m⁻² s⁻¹), transpiration rate (E, mmol m⁻² s⁻¹), and stomatal conductance (g_sw, mol m⁻² s⁻¹). iWUE (µmol CO₂ mol⁻¹ H₂O) and instantaneous WUE (µmol CO₂ mmol⁻¹ H₂O) were further calculated as the ratio between A_n and g_sw or A_n and E, respectively. The conditions in the leaf chamber during the measurement were set to 400 µmol mol⁻¹ CO₂, with actinic light adjusted to a value corresponding to the ambient photosynthetically photon flux density (PPFD) measured simultaneously by the Li600 device for each measured leaf. PPFD values thus ranged from 110 to 1150 µmol m⁻² s⁻¹ in the entire dataset. The leaf water potential (Ψ, MPa) was next measured on these leaves with a Scholander pressure (SoilMoisture Equipment Corp.). A 3-cm diameter disk was then sampled from the leaf and directly oven-dried for 1 week at 60°C. It was weighted to get its DW and used to collect NIRS spectra with the ASD laboratory spectrometer.

## 2.3 | Preprocessing of spectral data

All data analyses were handled in R v4.3.0 (R Core Team, 2023). Spectra from the ASD laboratory spectrometer were initially processed using the rmgap function (R/rchemo package) (Brandolini-Bunlon et al., 2023) to remove vertical gaps caused by differences between detectors (Ecarnot et al., 2013). Wavelengths between 350 and 400 nm were removed and excluded from spectral analyses due to their noisy signals (Ecarnot et al., 2013). The spectra were then smoothed using R/loess, with the neighborhood size controlled by an α value of 0.03. In order to express spectra from all devices in

reflectance and wavelength (nm), the MicroNIR absorbance (A) was converted into reflectance (R), as $R = 1/10^4$, and the NeoSpectra wavenumber ($\nu$, cm⁻¹) was converted into wavelength ($\lambda$, nm), as $\lambda = 10^7/\nu$ (Figure S1a). As the MicroNIR and NeoSpectra spectrometers have complementary ranges, with an overlap from 1350 to 1676 nm, a new, additional spectrum called MicroNIR_NeoSpectra was created for each leaf. Whole MicroNIR spectra from 950 to 1676 nm were retained due to their higher resolution than NeoSpectra spectra and combined with the 1676–2500 nm ranges measured with the NeoSpectra. The resulting gap at the junction of the two spectra was removed using R/rmgap from R/rchemo. All spectra (ASD, MicroNIR, NeoSpectra, and MicroNIR_NeoSpectra) were then preprocessed using standard normal variate (Barnes et al., 1989). This was followed by a first-order derivative using the Savitzky–Golay filter (R/sgolayfilt from the R/signal package [Savitzky & Golay, 1964]) (Figure S1b; Signal Developers, 2023). The polynomial order for the first-order derivative was set to 2, and the window sizes were set to 11, 15, 15, and 41 for MicroNIR, NeoSpectra, MicroNIR_NeoSpectra, and ASD, respectively, to account for their differences in resolution. Pre-processed spectra with SNV followed by first-order derivation were retained for PLSR modeling as the preprocessed methods allowed maximizing the $R^2$ and minimizing the root mean squared error (RMSE) in most predictive models compared with the other preprocessed methods.

## 2.4 | Data modeling and CV

Calibration models were constructed in order to relate high-throughput data from the spectrometers on the one hand and twenty-one variables, used as predictors, measured with the poro-fluorometer Li600 (Table S1) on the other hand to the traits of interest measured with low-throughput devices or methods (IRGA, pressure chamber, and disks weighing) using PLSR, as implemented in R/pls with the “kernelpls” method (Liland et al., 1999). The considered traits of interest were LMA, WCf, WQ, A_n, Ψ, iWUE, and WUE, using data from the greenhouse experiment. For the outdoor experiment, only LMA was considered as the other traits were not measured.

A total of 11 datasets were designed to address specific research questions while accounting for the experimental design and available data (Table 1; Figure S2). The first two datasets (A and B) aimed at evaluating the potential and complementarity of high-throughput phenotyping tools (spectrometers and poro-fluorometer Li600) for predicting traits of interest. These datasets included the maximum number of available data from the greenhouse experiment. We have performed a double CV procedure with fourfold repeated 10 times in the inner loop and fivefold in the outer loop, selected based on cultivars rather than individual leaves to avoid potential overfitting. This double CV procedure was repeated 10
<table><caption>TABLE 1 Strategies to evaluate the robustness and the prediction quality of partial least square regression (PLSR) models.</caption><thead><tr><th rowspan="2">Rationale</th><th rowspan="2">Dataset</th><th rowspan="2">LT or HT</th><th rowspan="2">Traits</th><th colspan="5">Calibration</th><th colspan="5">Validation</th></tr><tr><th>Number of leaves</th><th>WD1</th><th>WD2</th><th>Outdoors</th><th>Total</th><th>Cross-validation settings</th><th>Nb iter.</th><th>Number of leaves</th><th>WD1</th><th>WD2</th><th>Outdoors</th><th>Settings</th></tr></thead><tbody><tr><td rowspan="4">Devices complementarity</td><td rowspan="4">A</td><td rowspan="4">HT</td><td>LMA,</td><td>483</td><td>481</td><td>473</td><td>-</td><td>1437</td><td>Nb fold</td><td>10 iter. inner</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>WC<sub>f</sub>,</td><td></td><td></td><td></td><td></td><td></td><td>CV</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>WQ</td><td></td><td></td><td></td><td></td><td></td><td>CV</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LMA, A<sub>it</sub>,<br/>Ψ, iWUE,<br/>WUE</td><td>37</td><td>36</td><td>36</td><td>-</td><td>109</td><td>CV</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">Robustness between experiments</td><td>C</td><td>HT</td><td>LMA</td><td>-</td><td>-</td><td>-</td><td>370</td><td>370</td><td>Fourfold</td><td>10</td><td>100</td><td>100</td><td>100</td><td>15</td></tr><tr><td>D</td><td>HT</td><td>LMA</td><td>124</td><td>123</td><td>123</td><td>-</td><td>370</td><td>Fourfold</td><td>10</td><td>100</td><td>100</td><td>100</td><td>15</td></tr><tr><td>E</td><td>HT</td><td>LMA</td><td>93</td><td>92</td><td>93</td><td>92</td><td>370</td><td>Fourfold</td><td>10</td><td>100</td><td>100</td><td>100</td><td>15</td></tr><tr><td>F</td><td>HT</td><td>LMA,<br/>WC<sub>f</sub>,</td><td>370</td><td>-</td><td>-</td><td>-</td><td>370</td><td>Fourfold</td><td>10</td><td>100</td><td>100</td><td>100</td><td>15</td></tr><tr><td rowspan="4">Robustness between water treatments</td><td>G</td><td>HT</td><td>LMA,<br/>WC<sub>f</sub>,</td><td>-</td><td>370</td><td>-</td><td>-</td><td>370</td><td>Fourfold</td><td>10</td><td>100</td><td>100</td><td>-</td><td>15</td></tr><tr><td>H</td><td>HT</td><td>LMA,<br/>WC<sub>f</sub>,</td><td>-</td><td>-</td><td>370</td><td>-</td><td>370</td><td>Fourfold</td><td>10</td><td>100</td><td>100</td><td>-</td><td>15</td></tr><tr><td>I</td><td>HT</td><td>LMA,<br/>WC<sub>f</sub>,</td><td>124</td><td>123</td><td>123</td><td>-</td><td>370</td><td>Fourfold</td><td>10</td><td>100</td><td>100</td><td>-</td><td>15</td></tr><tr><td>J</td><td>LT</td><td>A<sub>it</sub>, Ψ,<br/>iWUE,<br/>WUE</td><td>24</td><td>24</td><td>24</td><td>-</td><td>72</td><td>LOO</td><td>10</td><td>10</td><td>10</td><td>10</td><td>-</td><td>100</td></tr><tr><td rowspan="3">Predictive models</td><td>K</td><td>LT</td><td>LMA, A<sub>it</sub>,<br/>iWUE,<br/>WUE</td><td>34</td><td>34</td><td>34</td><td>-</td><td>102</td><td>Fourfold</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1</td></tr></tbody></table>

Abbreviations: A<sub>it</sub>, net CO₂ assimilation; CV, cross-validation; HT, high-throughput phenotyping; iWUE, intrinsic water use efficiency; LMA, leaf mass per area; LOO, leave-one out; LT, low-throughput phenotyping; Nb iter., number of iterations; WC<sub>f</sub>, water content; WD1, moderate water deficit treatment; WD2, severe water deficit treatment; WQ, water quantity; WUE, instantaneous water use efficiency; WW, well-watered treatment; Ψ, leaf water potential.
times. The next three datasets (C, D, and E) were designed to assess model robustness across greenhouse and outdoor experiments for the prediction of LMA (the only trait available in both experiments), while the following four datasets (F, G, H, and I) were designed to assess model robustness across water treatments (within the greenhouse experiment) for the prediction of LMA, WC<sub>f</sub>, and WQ. All these datasets (C to I) included a fixed number of 370 leaves for the calibration, which were evenly and randomly sampled within the different experimental conditions: outdoors (C), greenhouse (D), outdoors and greenhouse (E), WW (F), WD1 (G), WD2 (H), and the three water treatments (I). Model training for these calibration sets was done through fourfold CVs repeated 10 times. Model evaluations were carried out within independent validation sets composed of random samples of 100 leaves repeated 15 times and specifically designed to evaluate prediction accuracy with respect to the experiment (greenhouse and outdoors) or the water treatment (WW, WD1, and WD2). Dataset J was designed to evaluate model robustness across water treatments within the greenhouse experiment for the prediction of A<sub>n</sub>, Ψ, iWUE, and WUE. This dataset was set to include a total of 72 and 30 leaves in the calibration and validation sets, respectively. These leaves were equally distributed in each water treatment. Since the number of leaves within the calibration set was rather low, model calibration for this dataset was done with leave-one-out (LOO) CV. Finally, a dataset (K) was designed for predicting traits of interest within the greenhouse experiment and further assessing the genetic variability of predicted values (see hereafter). This dataset included a total of 102 leaves within the calibration set evenly distributed per water treatment, and in this case model training was done through a fourfold CV scheme.

Whatever the dataset, PLSR models were always built with a maximum number of latent variables set at 20 for spectrometers and 10 for the Li600. The optimal number of latent variables was determined by minimizing the average RMSE over the iterations of the repeated CVs (datasets A to J) or directly the RMSE for the LOO CV (dataset J). Model quality was evaluated either within the CV (datasets A and B) or using validation (testing) sets (datasets C to J) using the R² computed as the squared correlation between observed and predicted values, the RMSE, or its normalized version to allow comparisons between traits (normalized root mean squared error [NRMSE]). Significant differences between model performances across experimental conditions or water treatments were assessed with a Tukey honestly significant difference post hoc test, as implemented in R/stats.

**2.5 | Heritability of predicted traits**

PLSR models (R²<sub>cv</sub> ≥ 0.55) calibrated with the dataset K were used to predict traits within the greenhouse experiment using

high-throughput measurements (fluo-porometers and NIRS data). The following mixed-linear model was fitted to the predicted values per water treatment in order to decompose their phenotypic variation using R/lme4 (Bates et al., 2015): $Y_{ij} = \mu + G_i + \Sigma A_{ij} + \varepsilon_{ij}$, where $Y_{ij}$ represented the vector of predicted values per water treatment, $\mu$ was the overall mean, $G_i$ was the random effect of the cultivar $i$, $\Sigma A_{ij}$ was a sum of fixed effects of experimental covariates measured on the plant $j$ from the cultivar $i$, and $\varepsilon_{ij}$ was the random residual effect. The fixed effects included date of measurement, time of measurement, time elapsed between the last irrigation of the plant and the measurement, duration between the entrance of the plant into the platform and the measurement, environmental conditions as the average of the 10 probes (temperature, PPFD, relative humidity, and vapor pressure deficit), and plant density around each plant. For each trait within each water treatment, a model selection was carried out to retain only the significant fixed effects ($\alpha = 0.05$) using R/rutilstimflutre (Flutre & Chabrault, 2019). Variance components in the selected model were used to estimate broad-sense heritability per water treatment as $H^2 = \frac{\sigma_G^2}{\sigma_G^2 + \sigma_E^2/n}$ with $\sigma_G^2$ the genotypic variance, $\sigma_E^2$ the residual variance, and $n$ the mean number of repetitions (leaves) per cultivar within each water treatment, typically around 2.

**3 | RESULTS**

**3.1 | Variability of leaf traits**

The lowest coefficient of variation was observed for WC<sub>f</sub> (<5%), regardless of the water treatment. LMA and WQ showed intermediate coefficients of variation (between 15% and 22%), with generally slight differences between water treatments and experiments (Table S2). LMA and WQ were positively correlated, with their correlation coefficient increasing from 0.63 to 0.73 with the intensity of water deficit in the greenhouse experiment (Figure S3a-c). LMA and WC<sub>f</sub> were negatively correlated, regardless of the water treatment, with correlation coefficient decreasing from -0.57 to -0.48 when water deficit increased. The correlation between WC<sub>f</sub> and WQ was lower but significantly positive, with values close to 0.25 (Figure S3a-c).

$A_n$, Ψ, iWUE, and WUE displayed more variability than LMA, WC<sub>f</sub>, and WQ (Figure S4), as underlined by higher coefficients of variation (Table S2). $A_n$ was the trait whose coefficient of variation was the most affected by the water treatment, increasing with the intensity of water deficit. Conversely, the coefficient of variation of iWUE decreased with increasing water deficit. $A_n$ was not significantly correlated with iWUE and WUE, whatever the water treatment (Figure S3d-f). iWUE was strongly correlated with WUE with values around 0.8 in all conditions (Figure S3d-f).
<figure><img src="image_9.png" /><figcaption>FIGURE 1 $R^2_{cv}$ values computed in the cross-validation procedure of leaf traits according to the device. (a) High-throughput measured traits: leaf mass per area (LMA), water content (WC<sub>f</sub>), and water quantity (WQ) (dataset A in Table 1). (b) Low-throughput measured traits: Net CO2 assimilation ($A_n$), leaf water potential ($Ψ$), intrinsic water use efficiency (iWUE), and instantaneous water use efficiency (WUE) (dataset B in Table 1). Each point in the boxplot corresponds to the $R^2_{cv}$ of one of the 10 iterations of the double cross-validation (as detailed in Table 1).</figcaption></figure>

## 3.2 | Devices complementarity for the prediction of leaf traits

Overall, spectra of the three spectrometers collected in the greenhouse experiment (datasets A and B, Table 1) were highly effective in predicting traits such as LMA, WC<sub>f</sub>, and WQ. The coefficient of determination computed within the CV procedure consistently exceeded 0.67 (Figure 1a and Figure S5a-c) with almost no variability across the CV repetitions. Nevertheless, significant differences in $R^2_{cv}$ were found between devices (Figure 1; Table S3). The ASD spectrometer on dry leaves yielded the most accurate models to predict LMA compared to micro-portable spectrometers used in situ (Figure 1 and Figure S6a). Conversely, the best models for predicting WC<sub>f</sub> and WQ were established with spectra from micro-portable spectrometers collected in situ (MicroNIR, NeoSpectra, and MicroNIR_NeoSpectra in Figure 1a). Although model performances were almost similar for these three modalities, slightly better predictions were achieved by combining the wavelengths from MicroNIR and NeoSpectra. The PLSR models built with all 21 variables from the Li600 device (Table S1) were unable to predict LMA, WC<sub>f</sub>, and WQ ($R^2_{cv} \le 0.1$).

Regarding the prediction of $A_n$, $Ψ$, Iwue, and WUE with spectrometers, $R^2$ values remained low and not higher than 0.27 whatever the device considered (Figure 1b). In contrast, the models built with variables from the Li600 device for predicting $A_n$ displayed an $R^2_{cv}$ of 0.82 (Figure 1b and Figures S5d and S6b). The Li600 device also seemed to capture relevant information for predicting iWUE and WUE, with $R^2_{cv}$ values of 0.48 and 0.3, respectively (Figure 1b and Figure S5f,g). The Li600 device, a poro-fluorometer, measured stomatal conductance ($g_{sw}$) and transpiration ($E$) in the same way

as the Li6800 device, which was used as a low-throughput reference. Consequently, these porometry variables measured by the two devices were highly correlated, with correlation coefficients of $r \ge 0.83$ (Figure S7). The prediction accuracies of WUE using either the Li600 or the MicroNIR_NeoSpectra were not significantly different (Figure 1b; Table S3). $Ψ$ was poorly predicted, regardless of the device used, with an $R^2_{cv}$ always below 0.24 (Figure 1b and Figure S5e). The large variability in $R^2_{cv}$ or NRMSE<sub>cv</sub> between the different CV sets was likely due to the small number of leaves included in each set (dataset B in Table 1).

To further investigate the effect of calibration set size on model accuracy, we compared PLSR models built with low- (109 leaves) and high-throughput (1437 leaves) measurements of LMA. As expected, models built within the low-throughput calibration set displayed significantly lower $R^2_{cv}$ than models built within the high-throughput calibration set (by 0.08-0.15) (Figure S8a). This might partly be due to a lower range of variation in the low-throughput calibration set compared to the high-throughput calibration set (Figure S3h). This point is confirmed by the RMSE<sub>cv</sub> values, which displayed low differences between low- and high-throughput calibration sets with values always lower than 0.42 mg cm<sup>-2</sup> (Figure S8b).

## 3.3 | Prediction robustness across experiments

The prediction accuracy for LMA was significantly lower within the outdoors than the greenhouse validation set (Figure 2), with median $R^2$ values ranging from 0.30 to 0.86 within the outdoors validation set and from 0.78 to 0.90
<figure><img src="image_10.png" /><figcaption>**FIGURE 2** Prediction quality of leaf mass per area (LMA) depending on whether spectra were collected in outdoor or greenhouse experiments. The top panel corresponds to the $R^2$ and the bottom panel corresponds to the root mean squared error (RMSE) (in mg cm⁻²) of the prediction. Each point in the boxplot represents one of the 15 iterations of the validation (datasets C, D, and E in Table 1). Different calibration sets are highlighted on the x-axis of each panel. Boxplots with the same letters within one facet are not significantly different (*p* > 0.05). exp.: experiment.</figcaption></figure>

within the greenhouse validation set, whatever the device considered. The ASD device used on dried disks yielded the most robust models for predicting LMA, regardless of the calibration and validation sets (Figure 2). Conversely, the models built with micro-portable spectrometers on fresh leaves, especially the MicroNIR, appeared to be impacted by the environmental conditions in which they were calibrated and validated (Figure 2). This was particularly the case when models were calibrated with greenhouse data and validated on data collected outdoors. For MicroNIR, NeoSpectra, and MicroNIR_NeoSpectra devices, the performance of models to predict LMA in outdoor conditions was similarly good whether calibrated with outdoor or greenhouse_outdoor calibration sets (Figure 2). Models calibrated with outdoors data remained accurate to predict greenhouse data since $R^2$ was greater than 0.78 and RMSE was lower than 0.8 mg cm⁻². However, the prediction of greenhouse data using an outdoors calibration set significantly reduced $R^2$ and RMSE values compared to greenhouse or greenhouse_outdoor calibration

sets. This might partly be due to a slightly lower coefficient of variation for LMA in outdoor (16.7) than in greenhouse (between 20.5 and 21.4) experiments (Table S2; Figure S4h,i), which could be due to differences in environmental conditions, especially the difference in PAR, which was three times greater outdoors than in the greenhouse.

## 3.4 | Prediction robustness between soil water treatments

PLSR models were compared between water treatments for LMA, WC~f~, and WQ as these three traits were available on the entire diversity panel (datasets F to J in Table 1). Partitioning the validation set according to the water treatment had more influence on model performance than partitioning the calibration set (Figure S9). We thus decided to focus our analysis on the comparison of PLSR models calibrated with leaves from the three water treatments (Figure 3).
<figure><img src="image_11.png" /><figcaption>FIGURE 3 Prediction quality of partial least square regression (PLSR) models for leaf mass per area (LMA), water content (WC~f~), and water quantity (WQ) depending on the validation set. Models were calibrated using the WW + WD1 + WD2 calibration set (dataset I in Table 1) and the MicroNIR_NeoSpectra and ASD devices. Subpart (a) corresponds to the R² and subpart (b) corresponds to the root mean squared error (RMSE) in the validation set. Each point in the boxplot represents one of the 15 iterations of the validation (as detailed in Table 1). The blue dots and boxes correspond to the well-watered treatment (WW); orange dots and boxes to the moderate water deficit treatment (WD1); red dots and boxes to the severe water deficit treatment (WD2); and pink dots and boxes to all WW, WD1, and WD2 treatments. Boxplots with the same letters within one facet are not significantly different (*p* > 0.05).</figcaption></figure>

$R^2$ of predictive models was more impacted by the water treatment (Figure 3a) than RMSE (Figure 3b). For LMA, $R^2$ was significantly lower within WD2 compared to the WW and WD1 validation sets (Figure 3a). These differences in $R^2$ could be related to a decrease in the range of variation with the increase in water deficit (Figure S4a), since the corresponding RMSE values did not significantly differ between water treatment (Figure 3b). Similarly for WC~f~ prediction with MicroNIR_NeoSpectra, $R^2$ significantly decreased from 0.86 to 0.75 when water deficit increased, while RMSE did not significantly vary (Figure 3a,b). This influence of water treatment on trait prediction was not observed for

WQ, with $R^2$ values always close to 0.8 when using the MicroNIR_NeoSpectra (Figure 3a) and median RMSE values lower than 8.1 mg H~2~O, regardless of the water treatment (Figure 3b). In contrast, the ASD device, used on dried leaves, yielded a less accurate prediction of WQ with lower $R^2$ values and RMSE always higher than 9.3 mg H~2~O. For this device, prediction accuracy was significantly lower for the WD2 treatment as observed for LMA and WC~f~.

Because $A_n$, $\Psi$, iWUE, and WUE were phenotyped on a limited subset of leaves from the diversity panel, PLSR models were calibrated by gathering all water treatments, and they were evaluated separately for each water treatment (dataset
<figure><img src="image_12.png" /><figcaption>FIGURE 4 Prediction quality of partial least square regression (PLSR) models for net CO₂ assimilation ($A_n$) and leaf water potential ($Ψ$) depending on the validation set. Models were calibrated using the $WW + WD1 + WD2$ calibration set (dataset J in Table 1) and the MicroNIR_NeoSpectra, ASD, and Li600 devices. Subpart (a) corresponds to the $R^2$ and subpart (b) corresponds to the root mean squared error (RMSE) in the validation set. Each point in the boxplot represents one of the 100 iterations of the validation (as detailed in Table 1). The blue dots and boxes correspond to the well-watered treatment (WW); orange dots and boxes to the moderate water deficit treatment (WD1); red dots and boxes to the severe water deficit treatment (WD2); and pink dots and boxes to all WW, WD1, and WD2 treatments. Boxplots with the same letters within one facet are not significantly different ($p > 0.05$).</figcaption></figure>

J in Table 1). The high variability of model performances observed with the 100 resamplings of validation sets within each water treatment (Figure 4) was likely a consequence of the small number of leaves in each validation set. $A_n$ was poorly predicted with spectrometers alone regardless of the water treatment considered for the validation (Figure 4a and Figure S10). In contrast, Li600 measurements allowed a good prediction of $A_n$, without much influence of water treatments, with median $R^2$ higher than 0.72 and median RMSE lower than 1.62 µmol m⁻² s⁻¹ across all validation sets (Figure 4). Neither the spectrometers nor the Li600 alone could reliably predict $Ψ$ (Figure 4).

PLSR models built with Li600 measurements displayed a variable performance in predicting iWUE and WUE depending on the validation set. The validation set that included the three water treatments ($WW + WD1 + WD2$) showed intermediate prediction quality with median $R^2$ values of 0.52 and 0.46 for predicting iWUE and WUE, respectively (Figure 5a). If each treatment was considered separately in the validation set, the prediction was higher for the WD1 water treatment, followed by intermediate performance under WW and finally poor performance under WD2 (Figure 5). These results indicated that PLSR models built with the Li600 device could not be considered accurate enough to predict iWUE and WUE for
<figure><img src="image_13.png" /><figcaption>FIG URE 5 Prediction quality of partial least square regression (PLSR) models for intrinsic water use efficiency (iWUE) and instantaneous water use efficiency (WUE) depending on the validation set. Models were calibrated with the WW + WD1 + WD2 calibration set (dataset J in Table 1) with variables from the Li600 device for iWUE and WUE (as the ratio of Li6800 $A_n$ and E or $g_{sw}$ measurements). Subpart (a) corresponds to the $R^2$, subpart (b) corresponds to the root mean squared error (RMSE) expressed in µmol CO₂ mol⁻¹ H₂O for iWUE, and subpart (c) corresponds to the RMSE expressed in µmol CO₂ mmol⁻¹ H₂O for WUE. Each point in the boxplot corresponds to one of the 100 iterations of the validation (as detailed in Table 1). The blue dots and boxes correspond to the well-watered treatment (WW); orange dots and boxes to the moderate water deficit treatment (WD1); red dots and boxes to the severe water deficit treatment (WD2); and pink dots and boxes to all WW, WD1, and WD2 treatments. Boxplots with the same letters within one facet are not significantly different (*p* > 0.05).</figcaption></figure>

leaves subjected to WD2 treatment. On the other hand, spectrometers performed poorly in predicting iWUE (Figure S11), while MicroNIR and MicroNIR_NeoSpectra provided more relevant measurements to predict WUE under WD1, with a median $R^2$ close to 0.53 (Figure S11a).

## 3.5 Prediction of LMA, $A_n$, iWUE, and WUE using high-throughput measurements

We retained the PLSR models with a median $R^2$ higher than 0.55 in at least one validation set to make prediction within the entire panel while being trained with low throughput data (dataset K in Table 1). The prediction models for LMA used between three and 12 latent variables, had $R^2_{cv}$ higher than 0.76, and had RMSE$_{cv}$ lower than 0.4 mg cm⁻², irrespective of the water treatment (Figure S12a). Ten latent variables were retained to predict $A_n$ using the Li600 device (Table 2). The

corresponding model displayed good performance across all water treatments with $R^2_{cv}$ and RMSE$_{cv}$ of 0.8 and 1.59 µmol m⁻² s⁻¹, respectively. iWUE and WUE were predicted with an $R^2_{cv}$ of 0.47 and 0.44, respectively, across all water treatments. In agreement with previous results, when considering leaves from WW and WD2 water treatments, $R^2_{cv}$ did not exceed 0.53, whereas the $R^2_{cv}$ for WD1 leaves was above 0.73 (Figure S12c,d). Therefore, models built with Li600 data were used to predict iWUE and WUE for leaves under WD1 treatment only.

The heritability of measured LMA across the entire diversity panel ranged from 0.28 to 0.43 depending on the water treatment (Figure 6a). The prediction of LMA using spectrometers provided very similar heritability estimates ranging from 0.20 to 0.42. We also estimated genetic values from LMA predictions which showed high correlations (r ≥ 0.9) with the genetic values estimated with the observed phenotypes, whatever the water treatment considered (Figure 6c).
<figure><img src="image_14.png" /><figcaption>**FIGURE 6** Heritability of observed and predicted leaf traits under different water treatments. Subpart (a) corresponds to the broad-sense heritability estimates for measured and predicted leaf mass per area (LMA) according to the spectrometer used as a predictor; subpart (b) corresponds to the heritability estimates for predicted net CO₂ assimilation (Pred_An), predicted instantaneous water use efficiency (Pred_WUE), and predicted intrinsic water use efficiency (Pred_iWUE); subpart (c) corresponds to the distributions and correlations between genotypic values of measured LMA versus genotypic values of predicted LMA according to the spectrometer used as a predictor (MicroNIR_NeoSpectra or ASD). Blue bars represent the well-watered treatment (WW); yellow bars represent the moderate water-deficit treatment (WD1); red bars represent the severe water-deficit treatment (WD2). ***p-value ≤ 0.001.</figcaption></figure>

<table><caption>**TABLE 2** Partial least square regression (PLSR) criteria obtained for predictive models (dataset K in Table 1).</caption><thead><tr><th>Trait</th><th>Device</th><th>LV</th><th>R<sup>2</sup><sub>cv</sub></th><th>RMSE<sub>cv</sub></th></tr></thead><tbody><tr><td>LMA</td><td>MicroNIR</td><td>12</td><td>0.78</td><td>0.38</td></tr><tr><td>LMA</td><td>NeoSpectra</td><td>3</td><td>0.76</td><td>0.40</td></tr><tr><td>LMA</td><td>MicroNIR_NeoSpectra</td><td>4</td><td>0.82</td><td>0.34</td></tr><tr><td>LMA</td><td>ASD</td><td>8</td><td>0.83</td><td>0.34</td></tr><tr><td>A<sub>n</sub></td><td>Li600</td><td>10</td><td>0.80</td><td>1.59</td></tr><tr><td>iWUE</td><td>Li600</td><td>8</td><td>0.48</td><td>27.09</td></tr><tr><td>WUE</td><td>Li600</td><td>7</td><td>0.44</td><td>1.53</td></tr></tbody></table>

*Note*: PLSR models follows a cross-validation procedure using leaves directly measured with conventional methods to predict leaf mass per area (LMA), net CO₂ assimilation (A_n), intrinsic water use efficiency (iWUE), and instantaneous water use efficiency (WUE) on the diversity panel. LV corresponds to the optimal number of latent variables retained by the model. R²cv and RMSEcv were calculated for all water treatment together (102 leaves).

Predicted An, iWUE, and WUE had heritability ranging from 0.16 to 0.5, depending on the water treatment (Figure 6b).

## 4 | DISCUSSION

In this study, we explored the performance of models built with high-throughput devices to predict ecophysiological and morphological leaf traits across a large genetic diversity panel of *Vitis vinifera* varieties. Using a combination of large datasets collected on thousands of grapevine plants across different experiments and conditions, we were able to evaluate the robustness of predictive models with respect to the experimental conditions. Based on an analytical framework with dedicated and repeated calibration and validation datasets, we found that the prediction accuracy within the validation set containing leaves of plants grown under severe water deficit was generally lower than that for other water treatments. We also found that all devices (NIR spectrometers or poro-fluorometer) were efficient in predicting at least one trait. Finally, we found that the leaf traits predicted with NIRS and poro-fluorometry data captured a substantial part of the genetic variation, which paves the way toward their use in genetic studies.
<figure><img src="image_15.png" /></figure>

## 4.1 | High-throughput devices are complementary to predict a range of morphological and physiological traits

For too-small datasets or too-complex traits, repeated double CV is more efficient to obtain an accurate estimation of the prediction error compared to a simple CV procedure (Filzmoser et al., 2009). In our analyses, we performed 100 resampling iterations for evaluating the prediction of $A_n$, $Ψ$, Iwue, and WUE and observed that model $R^2$ was highly dependent on the iteration. While previous studies most often only made one iteration for cross-validating PLSR models (Van Wyngaard et al., 2021), our results underline the interest of repeating the CV procedure to avoid bias in model evaluation.

Spectrometers resulted in better performances than Li600 for the prediction of LMA, $WC_f$, and WQ, while the Li600 was more efficient for the prediction of $A_n$, Iwue, and WUE. The laboratory spectrometer (ASD) was found as the most accurate to predict LMA. Absorption bands between 1670 and 1830 nm and 2000 and 2200 nm, covered by this device, were previously reported as relevant for predicting leaf dry matter-related traits like protein, lignin, and cellulose contents (Cheng et al., 2011; Curran, 1989; Kawamura et al., 2008; Kokaly, 2001). The difference in predictive performance between the laboratory spectrometer and the portable spectrometers could also be due to the fact that the ASD device was collected on dried leaves, while spectra from MicroNIR and NeoSpectra devices were collected in situ. In order to avoid the confounding effect of the device and the fresh/dry status of leaves, it could be interesting to collect spectra on fresh leaves using the ASD device and spectra on dried leaves using MicroNIR and NeoSpectra devices. It is known that the presence of water in samples can dominate the signal, potentially reducing the detection of smaller peaks related to other components such as nitrogen (Masemola & Cho, 2019), protein, lignin, and cellulose, which could be essential for predicting LMA. LMA and $WC_f$ were negatively correlated, likely because leaves with a low LMA tend to have a high water content (Poorter et al., 2009), but we observed that the devices yielding the best PLSR models for predicting these traits differed. Indeed, in that case, contrary to LMA, water-related traits ($WC_f$ and WQ) were better predicted in situ using portable spectrometers. Moreover, water-related traits were better predicted using the combined wavelengths of MicroNIR and NeoSpectra than with either device alone. This is likely because their combined spectral range includes the two main water absorption bands around 1400–1440 nm and 1900–1950 nm (De Bei et al., 2011). To rule out potential overfitting of PLSR models related to the wide spectral range of the ASD device, we have tested restricting ASD wavelengths to that used by MicroNIR_NeoSpectra. We concluded

that the size of the ASD spectral range did not affect prediction quality for all traits (data not shown).

The poro-fluorometer device was found to provide good prediction of $A_n$. $A_n$ was phenotyped with the Li6800 device, an IRGA based on the same physiological processes, using gas exchange, as the poro-fluorometer device. $A_n$ could therefore be partially predicted using variables such as $g_{sw}$ and $E$. Moreover, previous studies showed that $A_n$ correlates with the fluorescence decline ratio under steady-state conditions with saturated light (Lichtenthaler et al., 2005). Additionally, $A_n$ was well predicted when combining the electron transport rate, also derived from leaf fluorescence measurements, with the leaf-to-air temperature difference, used as a proxy for stomatal closure (Coupel-Ledru et al., 2019; Han et al., 2022; Losciale et al., 2015). In our study, spectrometers could not accurately predict $A_n$, in contrast to a previous study on maize (Cotrozzi et al., 2020). However, at least three spectra from the same side of a leaf were used in the maize experiment, while in our study, two spectra (one on each side) were taken from the same spatial area of a given leaf.

We found that the Li600 device was able to predict iWUE and WUE for plants under a moderate water deficit. In contrast, spectral data generally failed to provide reliable predictions, except for WUE, which could be predicted using MicroNIR and the MicroNIR-NeoSpectra combination under moderate water deficit. With a larger sample size, Cotrozzi et al. (2020) reported fairly good predictive performance of NIRS models for iWUE and WUE in maize.

The leaf water potential is a physiological trait commonly used as an indicator of soil water deficit intensity (De Bei et al., 2011; Girona et al., 2006; Simonneau et al., 2017). In our study, the leaf water potential was poorly predicted, regardless of the device used. Experiments on maize have shown that the leaf water potential could be predicted by NIRS with an $R^2_{cv}$ of 0.63 on both greenhouse and field samples (Cotrozzi et al., 2020). Field experiments on grapevines have shown the interest of NIRS for the prediction of leaf water potential on Syrah and Cabernet Sauvignon (De Bei et al., 2011). However, in these studies, stem water potential was better predicted than leaf water potential perhaps because spectra, taken at a single spot on the leaf, are not representative of the spatial variability in stomatal conductance (Albasha et al., 2019; De Bei et al., 2011).

## 4.2 | The robustness of PLSR models depends on the traits and the plant growing conditions

LMA has been widely used across species and locations to investigate the robustness of predictive models when plants were subjected to different environmental conditions
(Ji et al., 2024; Serbin et al., 2019). In these studies, a consid-
erable decrease in model accuracy was found when a trained
PLSR model was applied to a validation set containing other
sites or experiments. Here, we consistently highlighted a
degradation of outdoors prediction using a greenhouse cali-
bration set, similar to recent reports on maize (Cotrozzi et al.,
2020). However, in this study, we focused exclusively on
LMA, as it was the only trait available on all plants across
both the greenhouse and outdoor experiments. It would be
interesting to investigate the effect of the experiment on other
traits, such as traits related to leaf gas exchange. In the present
work, we further aimed at deciphering the effect of the water-
ing conditions on the PLSR model's prediction accuracy. The
prediction of ecophysiological traits might indeed be affected
by soil water availability. While most published studies pooled
together data collected in different growing conditions in
the calibration and validation datasets (De Bei et al., 2011;
Ryckewaert et al., 2022), here we tested both the effects of cal-
ibrating PLSR models per water treatment and of validating
them under different water treatments. Our results highlighted
that the composition of the validation dataset was more influ-
ential than the calibration one. Validating PLSR models per
water treatment prevents an artificial inflation of R² value
caused by the inclusion of multiple water treatments, which
in turn increases the trait variance. This likely led to more
accurate PLSR model evaluations, which highlighted changes
in prediction accuracy for LMA, WC<sub>f</sub>, iWUE, and WUE
depending on the water treatments. Such variation in R² is
primarily due to the reduction in trait variance as underlined
by the smaller differences observed for RMSE across water
treatments.

In this study, leaves were phenotyped between the 81 and
89 BBCH growth stages. Previous conclusions were therefore
specific to these phenological stages. Studies of leaf function
at other phenological stages still need to be conducted to val-
idate or invalidate these observations throughout the growing
season.

## 4.3 | PLSR models allow capturing the genetic variability of leaf traits

High correlations between predicted and measured values do
not necessarily imply an accurate prediction of the genetic
variability of the traits, especially when the calibration set
includes multiple experimental conditions. In such cases, the
model might have been trained to predict differences between
experimental treatments rather than between plant-related
variations within a given treatment.

Our results demonstrated that the broad sense heritabil-
ity of predicted LMA was comparable to that of measured
LMA, underlying, at least for this trait, that predicted values
were as efficient as measured values for capturing the genetic
variability within each water treatment. Similar findings were

reported for chlorophyll content, nitrogen content, and spe-
cific leaf area, predicted using hyperspectral reflectance data
in maize grown under low and high nitrogen conditions (Grzy-
bowski et al., 2021). The heritability of predicted A_n, iWUE,
and WUE depended on the water treatment with higher her-
itabilities for A_n and iWUE under moderate water deficit
compared to WUE and other water treatments. One reason
to explain these results could be that our study was based
on a single leaf per plant, even though the choice of the leaf
was homogeneous between plants and chosen as well-exposed
and fully expanded. Increasing the number of measurements
is likely to increase trait heritability (Jablonszky & Garam-
szegi, 2024), but this will also extend the time needed to
perform the measurements, which may not be appropriate
in a high-throughput phenotyping strategy. To overcome this
problem, other methodologies based on contactless devices
could be tested, particularly using unmanned aerial vehi-
cles equipped with hyper- or multi-spectral cameras. Such
approaches are valuable for capturing intra-organ or intra-
plant variability, as each pixel in the images contains spectral
information (Zhang et al., 2023). Thermal imaging could also
be used to assess leaves and canopy temperature as proxies
of water stress responses (Wen et al., 2023). Architectural
and functional traits have successfully been characterized
using LiDAR (light detection and ranging), multispectral, and
thermal imaging in a large core collection of apple trees
(Coupel-Ledru et al., 2019), highlighting a strong genotypic
effect on all traits, with high broad-sense heritability. A sig-
nificant effect of the water treatment was also detected on
most of the traits. However, when traits exhibit high spa-
tial variability at the fine scale, the accuracy of phenotyping
using imaging sensors is related to the image resolution. Air-
borne imaging may also be affected by background noise and
atmospheric scattering, which can alter the light absorption
features of pigments (Tattaris et al., 2016). Nevertheless, these
high-throughput methods are highly effective for phenotyp-
ing large numbers of plants and multiple environments in a
short time, thus reducing the error associated with the plant
circadian cycle. These phenotyping methods open prospects
for further analyses of the genetic determinism of ecophysio-
logical traits as recently shown in apple trees (Coupel-Ledru
et al., 2022).

5 | CONCLUSION

Our findings demonstrate the strengths of high-throughput
devices for predicting ecophysiological traits not directly
accessible at the same throughput in a diversity panel of
grapevine under varying experimental conditions. We have
highlighted the importance of validating PLSR models for
each experimental condition and not being restricted to a sin-
gle calibration/validation set. This approach can be applied
to other abiotic stresses, other populations, or even other
species. This would require sharing high-throughput datasets collected on multiple species, growth conditions, and environments to increase predictive models' robustness so that they can be applied more easily to a wide range of study cases. High-throughput phenotyping strategies can be integrated with imaging-based phenotyping or 3D modeling to improve the assessment of inter-plant variability and further enhance our understanding of plant responses to environmental stresses through genetic analyses. Overall our results open promising avenues for further deploying genome-wide association studies on traits predicted from high-throughput measurements. A crucial next step will be to test these models under field conditions to confirm their relevance to production conditions. They could then be easily used by breeders to characterize leaf function in plants undergoing selection.

## AUTHOR CONTRIBUTIONS

**Eva Coindre**: Data curation; formal analysis; investigation; methodology; resources; visualization; writing—original draft. **Romain Boulord**: Data curation; investigation. **Laurine Chir**: Data curation; formal analysis; investigation; writing—review and editing. **Virgilio Freitas**: Data curation; formal analysis; investigation. **Maxime Ryckewaert**: Methodology; writing—review and editing. **Thomas Laisné**: Investigation. **Virginie Bouckenooghe**: Investigation. **Maëlle Lis**: Project administration; resources. **Llorenç Cabrera-Bosquet**: Project administration; resources; writing—review and editing. **Agnès Doligez**: Funding acquisition; project administration; writing—review and editing. **Thierry Simonneau**: Funding acquisition; project administration; writing—review and editing. **Benoît Pallas**: Investigation; methodology; supervision; writing—review and editing. **Aude Coupel-Ledru**: Conceptualization; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing. **Vincent Segura**: Conceptualization; funding acquisition; investigation; methodology; project administration; supervision; writing—review and editing.

## ACKNOWLEDGMENTS

The authors are grateful to the INRAE "Domaine de Vassal" Grape Germplasm Repository for collection maintenance. We thank Freddy Gavanon and Mélyne Falcon for the plant management and the preparation of the greenhouse experiment. We thank Gaëlle Rolland for her help in the leaf phenotyping. We thank Martin Ecarnot and Frederic Compan for the access to the laboratory spectrometer ASD. We thank the ETAP (LEPSE), M3P (LEPSE) and DAAV (AGAP Institut) team members for providing help during the preparation and phenotyping of the grapevine panel. We thank Laurent Torregrosa for administration of ANR G2WAS project and Dominique This and Roberto Bacilieri for administration of Défi clé Vinid'Occ PlastiVigne.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

## DATA AVAILABILITY STATEMENT

The datasets supporting the conclusions of this article are available in the Recherche Data Gouv repository at https://doi.org/10.57745/WVAPOL. The scripts for prediction analyses are publicly available in GitLab: https://forgemia.inra.fr/eva.coindre/robustness HT prediction_ecophysiological traits.

## ORCID

Vincent Segura https://orcid.org/0000-0003-1860-2256

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SUPPORTING INFORMATION

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**How to cite this article:** Coindre, E., Boulord, R., Chir, L., Freitas, V., Ryckewaert, M., Laisné, T., Bouckenooghe, V., Lis, M., Cabrera-Bosquet, L., Doligez, A., Simonneau, T., Pallas, B., Coupel-Ledru, A., & Segura, V. (2026). Robustness of high-throughput prediction of leaf ecophysiological traits using near infrared spectroscopy and poro-fluorometry. *The Plant Phenome Journal*, **9**, e70059. https://doi.org/10.1002/ppj2.70059