Farming is facing many economic challenges in terms of productivity or cost-effectiveness, as well as an increasing labour shortage partly due to depopulation of rural areas. Current systems still have significant drawbacks in areas such as flexibility, efficiency, robustness, sustainability, high operator cost and capital investment. Furthermore, reliable detection, accurate identification and proper quantification of pathogens and other factors, affecting both plant and animal health, are critical to be kept under control so as to reduce economy expenditures, trade disruptions and even human health risks. AFarCloud provides a framework able to promote precision farming solutions, realized through the development of specialized Cyber Physical Systems and integration with other disciplines concerning data mining and analyses with agronomic protocols, by using not only new robotic platforms but also incorporating to the aforementioned framework the legacy systems already deployed in the farms.
The AFarCloud project aims to make farming robots accessible to more users by:
- Enabling farming robots to work in a cooperative mesh, thus opening up new applications and ensuring re-usability, as no specialized vehicles are needed because heterogeneous standard vehicles can combine their capabilities
- Increasing the autonomy of farming robots and improving their usability
AFarCloud achievements will be demonstrated in 2 field tests in both cropping (general horticulture, grass sub-scenario and seed potato sub-scenario) and livestock scenarios (food management and livestock movement control).
AFarCloud outcomes will strength partners’ market position boosting their innovation capacity and addressing industrial needs both at EU and international levels.