Fostering precision agriculture and livestock farming through access to High Performance Computing and Big Data analytics.

Donau Soja is participating in the EU-funded H2020 project CYBELE. Starting January 2019, 31 organisations are collaborating to generate innovation in the European agriculture and food sector through the convergence of High Performance Computing infrastructure (HPC), big data analysis, machine learning methods, cloud computing and the IoT. It will bring social, environmental and economic benefits, such as reduced scarcity and increased food security.

Soya yield and protein prediction model for soya fields

Together with Biosense, Donau Soja is contributing to the development of a yield and protein prediction model for soya fields. Using satellite images, as well as soil analysis, weather and further data, a software model is trained to predict the quantity and quality of soya fields harvests. Biosense applies advanced methods including machine learning algorithms in order to process as much available data as possible and ensure the model is solid and reliable. Donau Soja’s task is to collect data for ‘on the ground’ verification, using georeferenced data on real yields and protein content, gathered using the principles of crowdsourcing. Satellite images and data measured in the field will be combined to create a proper model which allows reliable statements to be made on the expected yield and protein content of the harvest. This requires huge processing power and smart algorithms, as the data must be analysed as a time series and covers the entire area in which European soya is produced (4.2 million ha in 2018).

In a further step, Donau Soja will design and validate business models for the soya harvest prediction model and further applications based on this model. Optimising sustainable European protein production through an overall increase in efficiency while simultaneously decreasing the environmental impact is a strong motivator for supporting the Cybele project.

More information is available on the project website.

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