Overview
Hybrid ML & Earth Observation for multi-stressor crop disease detection
HYDRA-EO is a European Space Agency project that develops an operational framework to detect, quantify, and differentiate abiotic and biotic crop stress. The project targets priority plant pathogens, including Flavescence Dorée in grapevine, Potato Virus Y in potato, and fungal diseases such as Septoria, while also assessing stress interactions under variable water and environmental conditions.
HYDRA-EO integrates physically based radiative transfer modelling approaches with machine learning algorithms to identify the spectral and spatial resolutions required to capture biotic stress signals effectively. By combining hyperspectral, thermal, and multi-sensor Earth Observation data from field, airborne, and satellite platforms, the project delivers early and reliable indicators to support agricultural resilience and evidence-based decision-making across Europe.
Section · Overview
Understanding crop stress across spectral and spatial scales
HYDRA-EO develops an operational framework to detect, quantify, and differentiate abiotic and biotic crop stress across multiple spatial and spectral scales. The project focuses on priority plant pathogens, including Flavescence Dorée in grapevine, Potato Virus Y in potato, and fungal diseases such as Septoria, while also assessing interactions between disease pressure, water stress, and environmental variability.
By integrating physically based radiative transfer modelling approaches with machine learning algorithms, HYDRA-EO identifies the spectral and spatial resolutions required to robustly capture biotic stress signals. The combination of hyperspectral, thermal, and multi-sensor Earth Observation data from field, airborne, and satellite platforms enables the delivery of early and reliable indicators to support agricultural resilience and evidence-based decision-making across Europe.
Phenotyping potato site
Section · Overview
Interactive maps
HYDRA-EO investigates crop stress processes across contrasting agroecosystems in Southern and Central Europe. The interactive map highlights the core study areas, including pistachio and olive orchards in Spain, a potato cropping system in the Netherlands, and an vineyard and alfalfa field sites in Central-Northern Italy. These sites represent diverse climatic conditions, crop types, and management practices, providing a robust basis to analyse how biotic stressors—such as Flavescence Dorée, Potato Virus Y, and fungal diseases like Septoria—interact with water stress and environmental variability.
UAV / airborne
UAV & airborne processing
Radiometric and geometric correction of UAV and airborne hyperspectral and thermal data, including mosaicking, BRDF normalisation, and co-registration with field plots. Output products include surface reflectance cubes, temperature maps, and quality masks.
Satellite EO processing
Satellite EO processing
Sentinel-2, PRISMA, and EnMAP workflows (e.g. Sen2Cor atmospheric correction, spectral resampling, tiling, and time-series aggregation), harmonised across sites for crop–stressor analysis and EO upscaling.
RTM library & hybrid ML
Hybrid ML methods
PROSAIL and SCOPE forward simulations via ToolsRTM and SCOPEinR, LUT generation, plant trait retrieval, and hybrid machine learning models trained on combined RTM–EO features for crop stress detection.
© 2026 HYDRA-EO.
HYDRA-EO is funded by the European Space Agency (ESA) under the EXPRO+ Tender “Crop Multiple Stressors, Pests and Diseases”. Action 1-12684.
