Website for the HYDRA-EO – Hybrid Machine Learning & Earth Observation for Multi-Stressor Crop Disease Detection project, funded by the European Space Agency (ESA, EXPRO+ Tender, Action 1-12684).
Project website (GitHub Pages)
https://CCGCAM.github.io/HydraEO
HYDRA-EO is an ESA-funded project that combines hyperspectral, thermal and fluorescence data with radiative transfer models (RTMs) and hybrid machine learning to detect biotic and abiotic stress in key crops across Spain, Italy and the Netherlands.
The project is coordinated by the Laboratory of Geo-information Science and Remote Sensing, Department Environment Sciences, Wageningen University (WU-DES), together with:
- WENR – Wageningen Environmental Research, Wageningen University and Research (Wageningen, Netherlands)
- CNR-IBE Consiglio Nazionale della Ricerca, Institute of BioEconomy (Fiorenze, Italy)
- CIAG-IRIAF – Agro-environmental Research Centre (Ciudad Real, Spain)
This repository contains the static website for HYDRA-EO:
index.html: main project page (project description, objectives, scenarios, methods, open tools, consortium, news).docs/: documents, papers.assets/:figures, logos and icons used on the site.tools/: HTML pages describing open tools such as ToolsRTM and SCOPEinR.
The site is deployed via GitHub Pages and is intended as a public entry point for the HYDRA-EO project.
The HYDRA-EO website links to the following scientific software:
-
ToolsRTM – R package for accessing multiple radiative transfer models
GitLab: https://gitlab.com/caminoccg/toolsrtm -
SCOPEinR – R interface for SCOPE-style energy-balance and fluorescence simulations
GitLab: https://gitlab.com/caminoccg/scopeinr
If you use HYDRA-EO materials or tools, please acknowledge:
HYDRA-EO – Hybrid Machine Learning & Earth Observation for Multi-Stressor Crop Disease and Pest Detection (ESA EXPRO+ Tender, Action 1-12684).
The project is funded by the European Space Agency (ESA).