on 15 Apr 2018 1:58 PM

This year during the CAPIGI 2018 conference on Performance Agriculture a hackathon was organised as a side event, with a contribution from AGINFRA+ roject. The key challenge of this Rewarding Nature Hack was helping farmers demonstrate how their planting of more herb rich grasslands contribute to increasing biodiversity. One of the possible solutions is the use of Sentinel satellite imagery, i.e. Sentinel 1 synthetic-aperture radar data and Sentinel 2 multi-spectral data, as input to calculate vegetation indices (NDVI), do (crop) phenology curve fitting, and feed machine learning algorithms to classify grassland fields as more or less herb rich. Of course the short time available for a hackathon (< 32 hours, depending on how much sleep you need) only leaves room for exploring the topic and work on a small scale proof of concept. And it does not help that all participants usually work on their own tiny laptops without access to the storage and compute infrastructure needed for the task at hand. During this hackathon access was provided to the AgroDataCube (for dutch agriculture related data), and to Sentinel Hub and Google Earth Engine for working with the satellite data. But a Virtual Research Environment (VRE) like the AGINFRA+ Agro-Climate Modelling platform could fill in the missing parts (e.g. collaboration tools) and provide specific algorithms for agri-food related research that can be run ‘in the Cloud’. Once more of such functionality is operational in the VRE trying it out in a next hackathon will be a good opportunity to get lots of feedback in a short time.



The selected AGINFRA+ Use Cases will illustrate the benefits of applying the Science as a Service approach to pressing research questions from the corresponding research communities.


This community focuses on use cases aim to support the workflow of researchers, intermediaries and business analysts working on crop modelling, crop phenology estimation and yield forecasting, as well as related activities in the area of policy and decision support in food security, farm management advice and related activities.



This community focuses on use cases to support scientists in the multidisciplinary field of risk assessment and emerging risk identification as there is currently a strong need to create new technology-supported solutions that facilitate the knowledge integration processes relevant for these tasks.



This community focuses on use cases related to the high-throughput phenotyping large amount of data which need to be analyzed immediately for decision making. This aims to support phenomics researchers to select plant species and varieties which are the most adapted to specific environments and to global changes.


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