Introduction HYDRAS (HYdrology, Drones & RAinout Shelters) is a state-of-the-art, multi-sensor field phenotyping platform designed specifically for the investigation of the response of crops to drought stress. HYDRAS is equipped to perform both above- and belowground phenotyping and is equipped with a semi-automatic data acquisition and processing pipeline. These unique features, in combination with its dimensions (see below) allows modelling the responses of plants to shortage of water in a representative production environment. HYDRAS was developed to address two critical challenges in plant breeding for climate adaptation: 1. Access to information regarding the below-ground functioning of plants under water stress, and 2. Translation of results from basic plant science to realistic field conditions. Methodologies • Rainout shelters (#9, 300 m² each (27 in total) + irrigated control fields) that can be moved automatically to cover the crop during critical developmental phases in relation to drought. • UAV based above ground crop phenotyping (RGB, MS, Thermal, …). • Belowground phenotyping based on the use of Electrical Resistivity Tomography (ERT). • Expertise and data pipeline/engines to extract relevant crop traits. Output • Standardized phenotyping datasets, derived traits, … (e.g. biomass, WUE, resilience indicators, …) that support AI-model development, decision-support applications.Impact • Facilitate the access of the scientific community and industry to a state-of-the-art technology and infrastructure for developing and/or testing climate resilient varieties, management options and products, under field conditions. What we can offer • HYDRAS is open access (https://hydras.ilvo.be/en) and welcomes users through service contracts or collaborative research projects. It is also part of EU-projects for the financing of access to research infrastructures such as AgroServ. • Data sets on aboveground (UAV close remote sensing: RGB, MS, thermal) and belowground phenotyping using ERT for different crops. • Protocols for UAV-based and ERT-based phenotyping. What we need • New phenotyping/envirotyping technologies providing continuous/non-destructive measurements e.g. Soil N/nutrient content, new sensors, belowground phenotyping tools. • High resolution satellite imagery time series to link with UAV based close remote sensing. --- Introduction: VITO’s digital Earth Observation platforms - MAPEO, Terrascope, openEO, and CLIMTAG - integrate field, drone, satellite and climate data into a unified and scalable ecosystem for plant phenotyping. By combining phenotyping with EO digital services, these operational platforms support both research and agricultural applications. The system aligns with European research infrastructure (Emphasis), ensuring interoperability and collaboration. It positions digital phenotyping as a key driver of data-driven agriculture. Methodologies: • Digital Platforms: MAPEO (field & drone), Terrascope/openEO (satellite), CLIMTAG (climate indicators) • Federated system integrating multi-source data via standards, APIs, and cloud workflows • Enables scalable data exchange and time-series, multi-scale analysis • FAIR principles embedded: automated QC, phenotyping specific metadata, cloud technology for hosting and processing of data. Output: • FAIR datasets combining field, drone, and EO data for phenotyping applications • Multi-scale products: plot to regional insights • High TRL operational platforms designed to be used by plant researchers and industry • User interfaces + APIs for access, visualization, and integration • Training materials and workflows for broad user adoption Impact: • Improves efficiency, scalability, and reproducibility of phenotyping data • Links field observations with large-scale environmental data • Supports research, industry, and policymaking What I can offer: • Open access digital platforms for phenotyping and EO applications: MAPEO, Terrascope, openEO, CLIMTAG • Development of scalable, FAIR-compliant pipelines and database (STAC) • Integration of field, drone, and satellite data with latest computer vision AI analytics • Translation research outcome into user-ready tools, workflows, and training What I need: • Collaboration with phenotyping and agricultural experts to test, adapt and integrate our digital tools into their research. • Access to complementary datasets and connection with other digital infrastructures • Co-development of FAIR drone database: definition of standards and metadata structure

Source: Annual AgData Use Case Summit 2026 booklet.