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Our core competencies are capturing and analyzing remote sensing data, data modeling, field trials and the development of web-based agricultural services.
Together with our partners from industry and research, we bring innovative solutions to the market with the goal of increasing the efficiency and sustainability of agricultural field management.
Together with the following industry partners, we develop innovative solutions for agriculture.
Together with the following research partners, we develop innovative solutions for agriculture.
Our R&D activates are financially supported by the following institutions.
Together with our partners, we have created many new solutions. Some of our projects are briefly described here
AgriCircle is proud to be a part of the European Union’s agricultural initiative to simplify cross-platform data exchange.
The goal of ATLAS is to achieve a new level of interoperability of agricultural machinery, sensors and data services and to give farmers full control over their data. Within ATLAS, we are helping to build an open, distributed and extensible data platform that is secure and scalable.
Together with Fraunhofer Institut, AgriCircle leads the ATLAS initiative which is supported and shaped by many partners.
Project partners: Fraunhofer Institut, DLG, AEF, meteomatics, a.o.
Sponsor: European Commission
Status: Running until spring 2023
Soil zoning allows the identification of similar soil zones within a field and the detection of similar soil properties such as clay, sand, silt, and organic matter as well as pH and soil mineralization.
Knowing the properties of each zone, treatment can be optimized to increase soil health and crop yields.
Project partners: ESA, ETH Zurich, Lufa Northwest, DroneHarmony
Status: completed
Early detection of infestation in vineyards with sensor technology and data transfer to the AgriCircle precision farming platform.
Project partners: Danish Technological Institute, Fraunhofer Institut, ZHAW, Schmidheiny Wines
Sponsors: ICT Agri, Thomas Schmidheiny
Status: completed
Maps produced by machine learning show soil moisture with and without vegetation. Soil moisture values were further adjusted by a stable integration of the DAISY model.
Project partners: Rauch, Kuhn, Monroc, Vienna University of Technology
Status: completed
Based on hyperspectral and radar data we calculated plant indices which provide farmers with a better understanding for the application of plant growth regulators (PGR).
Project partners: Adama, Syngenta, ETH Zurich, GFZ Potsdam
Status: completed
This project was built on the foundation of field-specific soil zoning and vegetation zoning. Our maps show a productivity ranking of zones within a field. The zone with the highest rank is classified as the zone with the highest productivity.
Project partner: internal project
Status: completed
Under normal conditions, homogeneous texture is expected in field management, which means that all crops should grow at a similar rate. However, under certain conditions (e.g. disease infestation), the growth rate of crops may change.
The spatial anomaly maps show farmers which regions of their fields behave differently than other regions to help them take the necessary measures.
Project partner: internal project
Status: completed
Growth rate maps are used to identify healthy regions within a field. These maps allow farmers to see in which areas of their fields performance is too low or too high. This allows them to take the necessary precautions before the field’s yield potential drops.
Project partner: internal project
Status: completed
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