Farming is facing many economic challenges in terms of productivity and cost-effectiveness, as well as an increasing labour shortage partly due to depopulation of rural areas. 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 in order to reduce economic expenditures, trade disruptions and even human health risks.
AFarCloud will provide a distributed platform for autonomous farming that will allow the integration and cooperation of agriculture Cyber Physical Systems in real-time in order to increase efficiency, productivity, animal health, food quality and reduce farm labour costs. This platform will be integrated with farm management software and will support monitoring and decisionmaking solutions based on big data and real time data mining techniques.
The AFarCloud project also aims to make farming robots accessible to more users by enabling farming vehicles to work in a cooperative mesh, thus opening up new applications and ensuring reusability, as heterogeneous standard vehicles can combine their capabilities in order to lift farmer revenue and reduce labour costs.
The achievements from AFarCloud will be demonstrated in 3 holistic demonstrators (Finland, Spain and Italy), including cropping and livestock management scenarios and 8 local demonstrators (Latvia, Sweden, Spain and Czech Republic) in order to test specific functionalities and validate project results in relevant environments located in different European regions.
AFarCloud outcomes will strengthen partners’ market position boosting their innovation capacity and addressing industrial needs both at EU and international levels. The consortium represents the whole ICT-based farming solutions’ value chain, including all key actors needed for the development, demonstration and future market uptake of the precision farming framework targeted in the project.
Optimizing Parallel Task Execution for Multi-Agent Mission Planning (Mar 2023) Branko Miloradovic, Baran Çürüklü, Mikael Ekström, Alessandro Papadopoulos IEEE Access (IEEE Access 2023)
GMP: A Genetic Mission Planner for Heterogeneous Multirobot System Applications (May 2021) Branko Miloradovic, Baran Çürüklü, Mikael Ekström, Alessandro Papadopoulos IEEE Transactions on Cybernetics (IEEE TCyb)
Exploiting Parallelism in Multi-Task Robot Allocation Problems (Apr 2021) Branko Miloradovic, Baran Çürüklü, Mikael Ekström, Alessandro Papadopoulos 21st IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC 2021)
A Genetic Algorithm Approach to Multi-Agent Mission Planning Problems (Jan 2020) Branko Miloradovic, Baran Çürüklü, Mikael Ekström, Alessandro Papadopoulos Communications in Computer and Information Science (CCIS)
TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model (Nov 2019) Branko Miloradovic, Mirgita Frasheri , Baran Çürüklü, Mikael Ekström, Alessandro Papadopoulos The 22nd International Conference on Principles and Practice of Multi-Agent Systems (PRIMA'19)
Extended Colored Traveling Salesperson for Modeling Multi-Agent Mission Planning Problems (Feb 2019) Branko Miloradovic, Baran Çürüklü, Mikael Ekström, Alessandro Papadopoulos 8th International Conference on Operations Research and Enterprise Systems (ICORES 2019)