# An Integrated Multi-Scale Phenotyping Research Infrastructure: From plant imaging to metabolomics

André Pierre Marie Fabbri, Sabrina Mazzoni, Valentina Lazazzara, Francesca

Alderotti, Maddalena Grieco, Adriano Conte

► To cite this version:

André Pierre Marie Fabbri, Sabrina Mazzoni, Valentina Lazazzara, Francesca Alderotti, Maddalena Grieco, et al. An Integrated Multi-Scale Phenotyping Research Infrastructure: From plant imaging to metabolomics. International Conference on Sustainability in Energy and Buildings SEB-25, KES International, Sep 2025, Catania (IT), Italy. hal-05317641

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Submitted on 16 Oct 2025

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### Title: An Integrated Multi-Scale Phenotyping Research Infrastructure: From plant imaging to metabolomics

Authors: André Fabbri, Adriano Conte, Sabrina Mazzoni, Francesca Alderotti, Giulia Atzori, Cecilia Brunetti, Andre Daccache, Donatella Danzi, Maddalena Grieco, Matthew Haworth, Dilek Killi, Valentina Lazazzara, Giovanni Marino, Felicia Menicucci, Vincenzo Montesano, Mauro Centritto.

## 1 Abstract

Plant phenotyping research plays a key role in providing tools to identify productive species and sustainable practices for increasingly resource-limited environments. The growing interests of stakeholders and recent technological advances have fostered the development of research infrastructures to support multidisciplinary and high throughput research. In this paper, we present an integrated research infrastructure for plant phenotyping that includes various instruments providing complementary perspectives from physiological to metabolomics scales. This high throughput plant phenotyping infrastructure provides extensive multi-scale datasets using remote sensing sensors mounted on different platforms. Alongside these sensors, the necessary computational resources to process and analyse complex datasets have been constructed to share pipelines and results over the cloud.

## 2 Introduction

The Mediterranean region is considered a climate change hotspot (Cos et al., 2022). Rising temperatures, prolonged droughts, and an increase in extreme weather events are expected to occur more regularly than they have in the past (Essa et al., 2023; Spinoni et al., 2018); thus posing significant threats to crop productivity and ecosystem stability (Lennon, 2015; Singh et al., 2023). Additionally, the growing global population and the resulting increase in food demand presents a major challenge for agriculture to optimize food production and satisfy global demand (Hemathilake and Gunathilake, 2022; Tian et al., 2021). To ensure stable crop yields and maintain quality, advancements in technologies, their accessibility and the adoption of novel agricultural practices are essential (Hollósy et al., 2023). Plant phenotyping technologies will play a crucial role in understanding plant-environment interactions at various scales, translating this knowledge into improved crop management strategies (Pieruschka and Schurr, 2019) and enhancing crop resilience to climate change (Nguyen et al., 2025).
Recognizing the importance of this applied research field, the European Strategy Forum on Research Infrastructures (ESFRI) identified "Plant Phenotyping" as a priority for the European Research Area. As a result, the European Infrastructure for Multi-scale Plant Phenomics and Simulation (EMPHASIS) was included in the ESFRI 2016 Roadmap as a key infrastructure project aimed at developing and implementing a scalable pan-European plant phenotyping network. A growing need for integrated laboratories, greenhouse, and field research has led to significant investments in developing integrated and scalable research infrastructures at the EU level

In the last years, researchers from Italy's National Research Council's Institute for Sustainable Plant Protection (IPSP-CNR) as part of the Italian node of EMPHASIS, have been developing a novel and comprehensive Phenotyping Research Infrastructure (RI): the **Digital Ecosystems for MEtabolomics, plant Trait Research, and Imaging Systems Hub** (DEMETRIS-Hub). This includes a range of state-of-the-art, environmentally controlled platforms as well as advanced field-based systems, offering an integrated approach to plant phenotyping. Staff is based across the three different IPSP's research sites, and supported by both public and private agencies, at the national and international level. Specifically the ENI-IPSP Joint Research Centre "Acqua - Ipazia d'Alessandria" 2019-2024, the Infrastructure Development Project - CNR Biennial Intervention Program 2021-2022 and 2022-2023 the Italian Integrated Environmental Research Infrastructures System (ITINERIS, Next Generation EU - PNRR) and PHENOLAB 4.0 (European Regional Development Fund - ERDF Basilicata 2014-2020) This paper will describe some of the main components of this RI, keeping in mind that it is an ongoing project and part of other research infrastructures.

The DEMETRIS-Hub brings together a broad range of cutting-edge technologies and has been designed to support a comprehensive "top-down approach" to the investigation of plant responses to abiotic stresses. This strategy enables the integration of data from the macro-level of the crop canopy down to metabolomics data at tissue and cellular level. A such approach ensures a comprehensive understanding of plant behavior under environmental constraints and the establishment of efficient high-throughput methodologies.

The data-acquisition, analytical and computational tools that make up this integrated RI, have been specifically selected, implemented, and developed in order to fulfill the following objectives:

a. To integrate agronomic, morphological, physiological, and metabolomics data to characterize plant responses to abiotic and biotic stresses through a multi-scale approach.

b. To support the identification and selection of stress-resilient genotypes, contributing to the development of crop varieties adapted to climate changes or sustainable agricultural practices.
c. To develop harmonized and high-throughput phenotyping workflows suitable for multi-scale stress response assessment.

d. To develop protocols on data set interoperability, advanced data-fusion and data-harmonization, including machine learning analytical approaches.

## 3 Data-acquisition infrastructures

The DEMETRIS-Hub workflow begins with preliminary experiments conducted under controlled environment (CE) conditions, which allow for the evaluation of plant responses to specific treatments while minimizing the influence of uncontrolled environmental factors. These initial trials are essential for identifying promising genotypes and stress-response traits under standardized conditions. Following the CE phase, the workflow progresses to open-field (OF) experimentation, where data are collected at multiple spatial scales over time. Remote sensing technologies, including UAVs equipped with advanced sensors, enable high-resolution monitoring at the crop level. Proximal sensing systems are then employed to gather detailed information at the level of individual experimental plots or plant groups (Figure 1). Leaf-level physiological traits are assessed using portable instrumentation, providing ground-truth data for the validation of remote observations. The workflow concludes with in-depth analyses of sampled tissues, focusing on production of primary and secondary metabolites to uncover the molecular and biochemical basis of observed phenotypes. This integrated, multiscale approach represents a state-of-the-art infrastructure for digital agriculture and high-throughput phenotyping. It provides a comprehensive suite of tools to monitor plant performance from the cellular level to the whole field scale.

<figure><img src="image_2.png" /><figcaption>Figure 1 – overview of the DEMETRIS-HUB consisting in a CE Facility (a), UAV (b), two phenorovers for fluorescence (c), RGB, Thermal, Multispectral and Hyperspectral images (d) and a top view of the gantry crane HTP platform (e).</figcaption></figure>
## 3.1 A Controlled Environment facility for preliminary trials

The CE Facility is the PSI Growth capsule (GC) DUAL Twin/Hybrid (Photon Systems Instruments), a walk-in phytotron equipped with the PlantScreen XYZ robotic system, a sensor-to plant system equipped with RGB digital colour imaging, Kinetic chlorophyll fluorescence imaging, The system enables the monitoring of various phenotypic traits over time under controlled temperatures (4 to 40°C), humidity (40 to 80%) and light intensity (up to 2000 µmol m⁻² s⁻¹). Additionally, the facility includes a greenhouse system where plants are grown in individual pots placed on scales, allowing precise control of irrigation, weight monitoring, and transpiration dynamics for each plant. This level of precision is crucial for assessing the effects of various treatments (e.g. irrigation regimes, bio stimulants, fertilizers, and microbial inoculants) and for determining threshold levels or optimal application rates (e.g., volumes of water or nutrients) required to trigger defined physiological responses in the studied genotypes. Although it is widely acknowledged that results obtained under controlled conditions may not be directly transferable to field environments where multiple abiotic and biotic factors act concurrently with the experimental treatments (Langstroff et al., 2021; Poorter et al., 2016). CE phenotyping remains a valuable complementary approach to field-based trials. Controlled experiments provide critical insights that are often difficult to obtain in the field and can significantly improve the design of field trials by refining treatment protocols, identifying target traits, and anticipating plant responses under more variable conditions.

## 3.2 Open Field Facilities for High Throughput Phenotyping

To complement the Controlled Environment (CE) infrastructure, a series of high-throughput phenotyping platforms tailored to open-field (OF) applications have been developed. These platforms are equipped with a variety of non-destructive sensors, essential for capturing the spatial and temporal variability of crop status across extensive field areas (Chawade et al., 2019; Zhang et al., 2023). Among these sensors are optical devices such as multispectral and infrared thermography systems, used to detect canopy temperature and vegetation index measurements in the visible and infrared spectrum, as well as hyperspectral cameras, which allow the analysis of vegetation indices across a broad spectral range. Additionally, LiDAR (Light Detection and Ranging) technology is used to generate 3D models of vegetation.

The OF facility integrates both remote and proximal sensing platforms. Remote sensing is conducted using a fleet of five Matrice 300 drones (SZ DJI Technology Co., Ltd.), each equipped with real-time kinematics (RTK) systems. These UAVs can be fitted with synchronized RGB, multispectral, thermal, panchromatic (ALTUM-PT Micasense), hyperspectral, and LiDAR cameras. This configuration enables rapid scanning and acquisition of georeferenced imagery, facilitating the construction of orthomosaics that provide a snapshot of the entire experimental field. Image analysis allows the calculation of vegetation indices (e.g., NDVI) and structural parameters (e.g., fCover, plant height), which support the evaluation and comparison of treatment effects on plant growth at the plot level (Candiago et al., 2015).
To range from the field to the individual plant level, a ground-based system of proximal sensing instruments has been developed. Two field rovers, each designed for specific tasks ranging from fluorescence analysis to multispectral and hyperspectral imaging, are in use. The PSI FluorCam FC 1300-R Rover (Photon Systems Instruments) is equipped with a fluorescence camera to estimate Photosystem II (PSII) activity in response to biotic and abiotic stresses (Chen et al., 2024). The rover enables non-destructive in-situ measurements over a 2×2 m area with adjustable height (20 cm to 1.5 m). Its camera and light panels can be enclosed within a cabinet for better control of dark adaptation and actinic light conditions. The system is also equipped with a post-processed kinematic (PPK) GPS station for preliminary georeferencing, enabling repeated measurements on the same plant over time and monitoring of plant development throughout the growing season. A variety of standard and customizable measurement protocols can be applied (e.g., Fv/Fm, Kautsky induction, quenching analysis, light curves) to assess photosynthetic performance under various stress conditions. The second rover is a prototype developed to replicate, at ground level, the sensor suite used by the UAVs. It features a telescopic arm extendable up to 16 meters and is equipped with an ALTUM PT multispectral camera (for RGB, multispectral, and thermal imaging), a CUBERT ULTRIS X20 Plus hyperspectral camera, and a handheld GeoSLAM ZEB Horizon mobile LiDAR scanner.

A third component of the system is a gantry crane-pivot prototype, compatible with the sensors used by the two rovers. This motorized mobile structure can operate over a 4,000 m² area. Multispectral, hyperspectral, and LiDAR sensors can be mounted on a pantograph system at a height of 2.5 metres, moving along tracks to scan back and forth across experimental plots. The FluorCam fluorescence camera can also be mounted on the pantograph, with adjustable height to maintain optimal image acquisition distance according to plants growth. Adopting a sensor-to-plant approach, the gantry can host multiple sensors simultaneously and carry out repeated scans of the georeferenced field throughout the growing season, meeting the requirements of a high-throughput phenotyping infrastructure (Beauchêne et al., 2019; Kim, 2020). Three ground-based meteorological stations are employed to monitor key climatic parameters, including air temperature and humidity at a height of 2 meters, soil temperature and moisture at various depths, global solar radiation, and leaf wetness. All data are available in real time through a web-based platform (EVJA AG BV).

## 3.3 Portable instrumentation for ground truth validation

A suite of handheld instruments, considered the 'gold standard' for measuring leaf-level gas exchange, plant biometric traits, and nutrient status (Ainsworth et al., 2014; Friedel et al., 2020; Saathoff and Welles, 2021; Xiao et al., 2002), are employed during both preliminary trials in controlled environments (CE) and open-field (OF) experiments. These pointwise measurements serve as reference "ground truth" to validate the accuracy of biometric and vegetation indices derived from proximal and remote sensing platforms.

For gas exchange and photosynthetic fluorescence at the leaf level, the portable equipment includes five infrared gas analyzers (IRGAs): two CIRAS-3 and two CIRAS-4 units (PP Systems), and one LI-6800 (LI-COR). Additionally, three LI-600 porometer-fluorometers (LI-COR), each
equipped with an embedded barcode scanner, are used for rapid assessment of stomatal conductance (gs) and the quantum yield of photosystem II (ΦPSII) in light-adapted leaves.

To assess plant nutrient status, a Spad 502 Plus (Minolta) and a Dualex sensor (Force-A) are used to measure chlorophyll, flavonols, anthocyanins, and nitrogen content. For canopy structural traits, a LAI-2200C leaf area index meter (LI-COR) equipped with a GPS module is utilized; its data can be directly compared with UAV-derived metrics (e.g. fCover).

Finally, portable sensors for soil moisture monitoring, the TDR, TR85, and the Handy-TRASE are used to collect detailed, point-based measurements of soil water content, offering valuable contextual information to support the interpretation of gas exchange data.

A JB Hyperspectral Devices Fluorescence Box (FLoX) can also be mounted above the plant canopy. The FLoX uses a dual hyperspectral system to monitor Sun-Induced Chlorophyll Fluorescence (SIF). The FLoX allows measurements of SIF to gauge photosynthetic activity and the protective dissipation of excess energy over the canopy scale in an open field setting. This non-destructive monitoring of plant photosynthetic status can be used to assess plant performance over the long and short-term. SIF measurements will complement data derived from hyperspectral, multispectral, infrared thermography and LiDAR sensors.

## 3.4 Metabolomics platform

The phenotyping infrastructure is further strengthened by the integration of advanced metabolomics and volatilomics tools, which enable the detailed detection and quantification of biomolecules with agronomic and industrial relevance. The cell and tissue phenotyping platforms are equipped with a Fourier Transform Infrared (FTIR) spectrometer (IRXross AIM-9000, Shimadzu), which is employed for comprehensive metabolic profiling in various plant tissues. To facilitate single-cell metabolomic studies at the tissue-specific level, the platform also includes a laser microdissection system (LMD6, Leica), paired with a rotary microtome (RM2125, Leica), allowing for precise sampling of specific cell layer and tissue types under different genotype-by-environment conditions.

High-throughput metabolomics and volatilomic analyses are conducted using state-of-the-art instrumentation, including high-performance liquid chromatography coupled with diode array detector and quadrupole time-of-flight mass spectrometry (HPLC-DAD-QTOF-MS Agilent 6530C), gas chromatography-mass spectrometry (GC-MS Agilent 8890), and proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) equipped with a reactor for chemical ionization (VOCUS CI-TOF). These systems enable both targeted and untargeted analyses of a wide array of metabolites, both in plant extracts and volatile organic compounds emitted by plants, supporting the identification of key metabolic fingerprint associated with plant performance and stress responses. This platform has multiple applications for the study of plant metabolic pathways involved in plant-environment interactions and the analysis of plant bioactive compounds for human applications (food, nutraceutical and pharmaceuticals).
In addition, a fully automated phenotype microarray system (OmniLog, Biolog) is employed to conduct up to 1,920 phenotypic assays simultaneously. This platform facilitates the assessment of physiological responses in a broad range of microbial strains, thereby extending the functional phenotyping capabilities of the infrastructure to include interactions between plants and their associated microbiomes.

These analytical platforms are crucial for the precise characterization of crop genotypes exhibiting enhanced adaptability to environmental stressors, thereby supporting the selection of resilient varieties and contributing to climate change mitigation strategies.

# 4 Computational infrastructure

The instruments listed above each have their own processing unit and dedicated program to sample data singularly, introducing significant challenges in data management, metadata collection, and analysis. To address these challenges, the DEMETRIS-Hub plant phenotyping laboratory is setting up a common computational infrastructure to integrate these data source in a common platform through automated pipelines.

These datasets will serve as a foundation for applying machine learning and artificial intelligence (AI) approaches. By leveraging AI and deep learning, researchers will be able to synthesize large, multivariate datasets, facilitating more precise and rapid quantification of key physiological traits across many plant replicates, create bridges between the different levels of the workflow. The integration of AI-based tools will accelerate discoveries related to plant productivity, stress resilience, and trait improvement.

The overall experimental workflow needs different computational resources according to the automation level of the instrument or the maturity of the data-processes. This infrastructure is available for multiple level of usage from first prototype to proof tested open-source pipelines. Even in terms of management a twofold infrastructure has been designed to provide whether a small number of limited capacities on-premises computational resources for regular internal usage or scalable on-demand cloud environment for time limited computational process and share the research products with the community.

## 4.1 On-Premises infrastructure

The On-Premises infrastructure provides an internal computational environment for small trials and developing computational processes to manage and analyze data. This local infrastructure is meant for the regular usage of the instrumentation providing major control on its configuration, limited running costs and an off-line backup in case of network issues.

A couple of desktop workstations (CPU Intel Core i9 - RAM 64GB – GPU NVIDIA 4090) provide relatively high performance for single users and ease prototyping with complete control over the system while a high-end server workstation (CPU AMD Ryzen Threadripper PRO 5965WX -
RAM 256GB - GPU NVIDIA RTX A4500) is intended for sustained long computation, supporting multiple concurrent users or host licenses server. The on-the premises workstations have hardware specifications designed to run instrument specific software (Agilent Mass Hunter suite), photogrammetry software (Riegel RiUNITE, AgiSoft Metashape), statistics, machine learning or even deep learning training (RStudio, Matlab, Python).

A locally connected NAS stores ongoing data processes and provides a common exchange storage for all on-premises computers. Almost all resources are also accessible remotely through a common secure gateway.

## 4.2 Cloud platform

The cloud platform combines virtual research environments for online computation and data portal for storage in an integrated solution for plant phenotyping (Assante et al. 2021). This will actually be developed as part of existing EU's VRE's open science project. As such the FAIR principles are at the heart of its development, thus making both the datasets and the tools to process them Findable, Accessible, Interoperable, and Reusable (Wilkinson et al. 2016).

The virtual research environment provides an online collaborative platform dedicated to HTPP with a state-of-the-art data science integrated development environment (Jupyter, Rstudio) to punctually perform statistical analysis or machine learning training on demand. This online platform opens to provide the newly developed relevant pipelines as a standalone service and readily reuse them for the community or to support processes in the workflow (Oliviero et al. 2025).

The data portal will be a deploy the open-source information system OpenSILEX (Alice et al. 2022) that has been developed by the plant phenotyping community to store and expose phenotyping data. The adherence to FAIR principles ensures that datasets are properly documented, searchable, and available for future research, contributing to the collective efforts in advancing plant science and addressing global agricultural challenges. OpenSILEX is an ontology driven system that defines metadata according to reference vocabulary shared by the community. The stored metadata complies with the MIAPPE protocol (Papoutsoglou et al. 2020), shared by the plant phenotyping community and the system provides the open standard programming interfaces BrAPI to facilitate automatized interoperability with other existing systems (Selby et al. 2019).

As an integrated platform, both the virtual research environment and the data portal will be accessed through the EGI check-in system to ensure a transparent integration with existing identity and secure single-sign-on access.

# 5 CONCLUSIONS
Plant phenotyping is increasingly acknowledged as a cornerstone for the future of crop breeding and selection, offering critical tools to confront pressing global challenges such as food security, environmental sustainability, and climate resilience. The EMPHASIS plant phenotyping community is strengthening its infrastructures, tools and analysis to face these complex issues. In this context, the DEMETRIS-Hub represents a major achievement in enhancing the research capacity of PhenItaly node, positioning it at the cutting edge of plant science and agricultural innovation. A particular attention in the design of this infrastructure has been drawn to support efficiently the research by following the proposed objectives:

a. The range of state-of-the-art sensors in laboratory and open field provides a complementary view on plant status at various levels: agronomic, physiological and metabolic. Computational resources together with all these data allow the creation of joint datasets and open to new features to characterize plant responses to abiotic stresses.

b. High Throughput infrastructures automatize acquisition processes and scale up the number of different species assessed in parallel in controlled environment and open-field conditions. Comparative studies of plant characteristics permit us to identify or select stress-resilient genotypes and produce more robust datasets for the same species obtained in different environmental contexts.

c. Non-destructive camera sensors have been adapted to various facilities and carriers to provide the same kind of data at various resolutions (from plant level to the field) and according to different pipelines depending on the carrier (from UAV to gantry). This assortment of acquisition contexts provides a diversified benchmark to assess and harmonize high-throughput phenotyping workflows according to the experiment.

d. The twofold computational infrastructure supporting the data processes allows us to implement advanced data fusion and data harmonization pipelines from a prototype level up to a deployed online service ready to be integrated into another pipeline. The collected data and research outputs will be made available to the plant phenotyping community through a cloud platform, promoting open and collaborative research based on the growing demand for innovation and interdisciplinary approaches. Among the most urgent needs identified by the community are improvements in data analysis, the expansion of modelling capabilities, and the seamless integration of novel technologies into user workflows facilitated by collaborative virtual online environment as presented.

Though the skills required to operate this research infrastructure are diversified for each level of the described workflow and once fully implemented, efforts should be made to create interdisciplinary proof-tested pipelines and data-driven models to bridge the gap between different levels of measurements. The creation of a federated community working on high throughput plan phenotyping research infrastructure may help to foster the co-development of harmonized pipelines and discovery of efficient models.
## Funding

This study was funded by the European Union Next-GenerationEU—Piano Nazionale di Ripresa e Resilienza (PNRR): projects Agritech National Research Center (PNRR—Missione 4 Componente 2, Investimento 1.4—D.D. 1032 17 June 2022, CN00000022), and Italian Integrated Environmental Research Infrastructures System (PNRR—Missione 4, Componente 2, investimento 3.1—D.D. 130 21 June 2022, IR0000032). This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible.

## References

Ainsworth, E.A., Serbin, S.P., Skoneczka, J.A., Townsend, P.A., 2014. Using leaf optical properties to detect ozone effects on foliar biochemistry. Photosynth Res 119, 65–76.
https://doi.org/10.1007/S11120-013-9837-Y/TABLES/2

Alic, Isabelle, et al. "PHIS et OpenSILEX: système d'information pour des données FAIR." *Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM)*. 2022.
https://hal.science/hal-05060889v1

Assante, M., Boizet, A., Candela, L., Castelli, D., Cirillo, R., Coro, G., ... & Sinibaldi, F. (2021). Realizing virtual research environments for the agri-food community: The AGINFRA PLUS experience. *Concurrency and Computation: Practice and Experience*, 33(19), e6087, https://doi.org/10.1002/cpe.6087.

Beauchêne, K., Leroy, F., Fournier, A., Huet, C., Bonnefoy, M., Lorgeou, J., de Solan, B., Piquemal, B., Thomas, S., Cohan, J.P., 2019. Management and characterization of abiotic stress via phénofield® a high-throughput field phenotyping platform. *Front Plant Sci* 10, 427488. https://doi.org/10.3389/FPLS.2019.00904/BIBTEX

Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., Gattelli, M., Lucieer, A., Zarco-Tejada, P.J., Rascher, U., Bareth, G., Inoue, Y., Thenkabail, P.S., 2015. Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images. *Remote Sensing* 2015, Vol. 7, Pages 4026-4047 7, 4026–4047.
https://doi.org/10.3390/RS70404026

Chawade, A., Van Ham, J., Blomquist, H., Bagge, O., Alexandersson, E., Ortiz, R., 2019. High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture. *Agronomy* 2019, Vol. 9, Page 258 9, 258. https://doi.org/10.3390/AGRONOMY9050258

Chen, X., Shi, D., Zhang, H., Antonio Sánchez Pérez, J., Yang, X., Li, M., 2024. Early diagnosis of greenhouse cucumber downy mildew in seedling stage using chlorophyll fluorescence imaging technology. *Biosyst Eng* 242, 107–122.
https://doi.org/10.1016/J.BIOSYSTEMSENG.2024.04.013

Cos, J., Doblas-Reyes, F., Jury, M., Marcos, R., Bretonnière, P.A., Samsó, M., 2022. The Mediterranean climate change hotspot in the CMIP5 and CMIP6 projections. *Earth System Dynamics* 13, 321–340. https://doi.org/10.5194/ESD-13-321-2022

ESFRI Roadmap 2016 | www.esfri.eu [WWW Document], n.d. URL https://www.esfri.eu/roadmap-2016 (accessed 5.6.25).
Essa, Y.H., Hirschi, M., Thiery, W., El-Kenawy, A.M., Yang, C., 2023. Drought characteristics in Mediterranean under future climate change. npj Climate and Atmospheric Science 2023 6:1 6, 1–13. https://doi.org/10.1038/s41612-023-00458-4

Friedel, M., Hendgen, M., Stoll, M., Löhnertz, O., 2020. Performance of reflectance indices and of a handheld device for estimating in-field the nitrogen status of grapevine leaves. Aust J Grape Wine Res 26, 110–120. https://doi.org/10.1111/AJGW.12424

Hemathilake, D.M.K.S., Gunathilake, D.M.C.C., 2022. Agricultural productivity and food supply to meet increased demands. Future Foods: Global Trends, Opportunities, and Sustainability Challenges 539–553. https://doi.org/10.1016/B978-0-323-91001-9.00016-5

Hollósy, Z., Ma'ruf, M.I., Bacsi, Z., 2023. Technological Advancements and the Changing Face of Crop Yield Stability in Asia. Economies 2023, Vol. 11, Page 297 11, 297. https://doi.org/10.3390/ECONOMIES11120297

Kim, J.Y., 2020. Roadmap to High Throughput Phenotyping for Plant Breeding. Journal of Biosystems Engineering 45, 43–55. https://doi.org/10.1007/S42853-020-00043-0/FIGURES/4

Langstroff, A., Heuermann, M.C., Stahl, A., Junker, A., 2021. Opportunities and limits of controlled-environment plant phenotyping for climate response traits. Theor Appl Genet 135, 1. https://doi.org/10.1007/S00122-021-03892-1

Lennon, J.J., 2015. Potential impacts of climate change on agriculture and food safety within the island of Ireland. Trends Food Sci Technol 44, 1–10. https://doi.org/10.1016/j.TIFS.2014.07.003

Nguyen, H.T., Khan, M.A.R., Nguyen, T.T., Pham, N.T., Nguyen, T.T.B., Anik, T.R., Nguyen, M.D., Li, M., Nguyen, K.H., Ghosh, U.K., Tran, L.S.P., Ha, C. Van, 2025. Advancing Crop Resilience Through High-Throughput Phenotyping for Crop Improvement in the Face of Climate Change. Plants 2025, Vol. 14, Page 907 14, 907. https://doi.org/10.3390/PLANTS14060907

Oliviero, A., Lettere, M., Dell'Amico, A., and Pagano, P.: CCP: A Cloud Computing Platform for VREs in Earth Sciences, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18524, https://doi.org/10.5194/egusphere-egu25-18524, 2025. https://doi.org/10.5194/egusphere-egu25-18524

Papoutsoglou, Evangelia A., et al. "Enabling reusability of plant phenomic datasets with MIAPPE 1.1." *New Phytologist* 227.1 (2020): 260-273. https://doi.org/10.1111/nph.16544

Pieruschka, R., Schurr, U., 2019. Plant Phenotyping: Past, Present, and Future. Plant Phenomics 2019. https://doi.org/10.34133/2019/7507131

Poorter, H., Fiorani, F., Pieruschka, R., Wojciechowski, T., van der Putten, W.H., Kleyer, M., Schurr, U., Postma, J., 2016. Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field. New Phytologist 212, 838–855.

Saathoff, A.J., Welles, J., 2021. Gas exchange measurements in the unsteady state. Plant Cell Environ 44, 3509–3523. https://doi.org/10.1111/PCE.14178
Peter Selby, Rafael Abbeloos, Jan Erik Backlund, Martin Basterrechea Salido, Guillaume Bauchet, Omar E Benites-Alfaro, Clay Birkett, Viana C Calaminos, Pierre Carceller, Guillaume Cornut, Bruno Vasques Costa, Jeremy D Edwards, Richard Finkers, Star Yanxin Gao, Mehmood Ghaffar, Philip Glaser, Valentin Guignon, Puthick Hok, Andrzej Kilian, Patrick König, Jack Elendil B Lagare, Matthias Lange, Marie-Angélique Laporte, Pierre Larmande, David S LeBauer, David A Lyon, David S Marshall, Dave Matthews, Iain Milne, Naymesh Mistry, Nicolas Morales, Lukas A Mueller, Pascal Neveu, Evangelia Papoutsoglou, Brian Pearce, Ivan Perez-Masias, Cyril Pommier, Ricardo H Ramírez-González, Abhishek Rathore, Angel Manica Raquel, Sebastian Raubach, Trevor Rife, Kelly Robbins, Mathieu Rouard, Chaitanya Sarma, Uwe Scholz, Guilhem Sempéré, Paul D Shaw, Reinhard Simon, Nahuel Soldevilla, Gordon Stephen, Qi Sun, Clarysabel Tovar, Grzegorz Uszynski, Maikel Verouden, The BrAPI consortium, BrAPI—an application programming interface for plant breeding applications, *Bioinformatics*, Volume 35, Issue 20, October 2019, Pages 4147–4155, https://doi.org/10.1093/bioinformatics/btz190

Singh, B.K., Delgado-Baquerizo, M., Egidi, E., Guirado, E., Leach, J.E., Liu, H., Trivedi, P., 2023. Climate change impacts on plant pathogens, food security and paths forward. Nature Reviews Microbiology 2023 21:10 21, 640–656. https://doi.org/10.1038/s41579-023-00900-7

Spinoni, J., Vogt, J. V., Naumann, G., Barbosa, P., Dosio, A., 2018. Will drought events become more frequent and severe in Europe? International Journal of Climatology 38, 1718–1736. https://doi.org/10.1002/JOC.5291

Tian, X., Engel, B.A., Qian, H., Hua, E., Sun, S., Wang, Y., 2021. Will reaching the maximum achievable yield potential meet future global food demand? J Clean Prod 294, 126285. https://doi.org/10.1016/J.JCLEPRO.2021.126285

Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., ... Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. ScientificData, 3, 1–9. http://doi.org/10.1038/sdata.2016.18

Xiao, X., He, L., Salas, W., Li, C., Moore, I., Zhao, R., Frolking, S., Boles, S., 2002. Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields. Int J Remote Sens 23, 3595–3604. https://doi.org/10.1080/01431160110115799

Yang, Y., Saand, M.A., Huang, L., Abdelaal, W.B., Zhang, J., Wu, Y., Li, J., Sirohi, M.H., Wang, F., 2021. Applications of Multi-Omics Technologies for Crop Improvement. Front Plant Sci 12, 563953. 10.3389/fpls.2021.563953

Zhang, H., Wang, L., Jin, X., Bian, L., Ge, Y., 2023. High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing. Crop J 11, 1303–1318. https://doi.org/10.1016/J.CJ.2023.04.014