Project No: 15050
1 Mar 2015 - 31 Aug 2015
Panos Trakadas, SYNELIXIS SOLUTIONS (Greece)
Oliver Hensel, University of Kassel (Germany)
Jussi Nikander, Natural Resources Institute Finland (LUKE) (Finland)
AgriFI is a novel, FIWARE-based service, offered through the FIspace platform, that will provide the opportunity to farmers across Europe to: 1) get accurate and real-time predictions of possible diseases related to their crops and consequently activate rules and conditions that will trigger specific actions related to diseases, 2) get up-to-date information about diseases and nutrition recipes related to their crops and 3) capitalize on the FIspace platform to create a pan-European farmers’ community, sharing the same problems and discussing possible solutions.
AgriFI will be realized through the instantiation, adaptation, configuration and utilization of 11 FI-PPP enablers, including the 7 FIspace modules, 2 GEs and 2 SEs.
At the research farm of the University of Kassel three crops (potatoes, carrots & tomatoes) were grown in 2015 and 2016, accompanied by visual records of optional leaf diseases. Phytphthora infestans and Alternaria solani were monitored at potatoes in a cultivar trial, 2015 with low, 2016 with higher levels of infection.
Carrots and tomatoes were found with negligible symptoms of leaf diseases.
Two serious obstacles caused negative impacts on the course and result of the project: (a) The exclusion of the coordinating Greek partner after six months, technical support and maintenance of the system were afterwards suboptimal, (b) the failure of the sensor for leaf moisture measurements and missing automatic inspections of transmission rates and fixed levels of acceptable extremes.
Considering the course of the project it is too early to speak about potential exploitations of gained results. But the concept is still trend-setting and worth for further development. In a second period, if proposed and provided, with optimized technology i.e. automatic inspection and communication systems, in addition to pure technical aspects the know-how of experts for predicting models should be included in order to create practicable applications for the farming community.