An integrated modelling and learning framework for real-time online decision assistance in Swiss agriculture
At a glance
We are developing an agricultural risk decision assistant based on a unique model that can assess and visualize reliable weather and seasonal climate forecasts, soil data, and crop growth forecasts. Based on real-time and historical weather, climate, soil and crop data and novel learning algorithms, the system calculates expected weather and climate conditions and crop yields and supports farmers with its real-time and online app in terms of production costs, irrigation management, resources required, etc.
Gygax, Gregory; Schüle, Martin,
Schilling, Frank-Peter; Stadelmann, Thilo, eds.,
Artificial Neural Networks in Pattern Recognition.
9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020.
Lecture Notes in Computer Science ; 12294.
Available from: https://doi.org/10.1007/978-3-030-58309-5_19