Methods and technologies to provide proactive planning and controlling of cooperative agriculture processes, exemplified by silage maize harvesting.
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The aim of this joint project is the realization of an infrastructure to provide proactive support for agriculture processes, taking silage maize harvesting as an example of use.
|Duration:||01.08.2016 till 30.11.2019|
|Donee:||German Research Center for Artificial Intelligence GmbH|
Bundesministerium für Ernährung und Landwirtschaft
Bundesanstalt für Landwirtschaft und Ernährung
Projektträger Bundesanstalt für Landwirtschaft und Ernährung
|Grant number:||This research and development project is funded by the German Federal Ministry of Food and Agriculture (BMEL) grant no. 2815700915.|
|Partner:||CLAAS E-Systems KGaA mbH & Co KG, CLAAS Selbstfahrende Erntemaschinen GmbH, 365 Farmnet GmbH & Co KG, green spin GmbH, Hochschule Bochum, 52° North Initiative for Geospatial Open Source Software GmbH|
|Application Field:||Agricultural Robotics|
The key component is the integration of data from sources that are already available, yet unused in this application scenario so far. The data comes from farm management systems, as well as the machines themselves, public geo-information infrastructures (such as Copernicus) and other external sources (e.g., harvest forecasts). This data is made accessible and provided to the users as complementary services: One service takes over the semi-automated planning of a harvesting campaign, based on information such as yield and soil trafficability. Another service performs the dynamic planning of all machines, based on current machine data and spatially differentiated yield prediction. A third service provides means for a proactive adjustment of the harvester, based on the integration of environment information, the spatial distribution of the yield and the current machine data. This way, the crop flow is stabilized, and the machine can be used in a more efficient and safer way. The developed services are provided by an open, service orientated software architecture, which is specified and implemented in this project. It heeds national and international standards from agricultural and geoinformation technology, as well as general IT standards. Work on the project prospective.HARVEST is done in the context of the DFKI competence center Smart Agriculture Technologies (CC-SaAT / saat.dfki.de).