FFN-ii: Location Aware System Deployment in the two Experimental Sites

Project acronym:
Project title:
Commercialization of an Automated Monitoring and Control System against the Olive and Med Fruit Flies of the Mediterranean Region
2.1 Technological transfer and commercialisation of research results
Title of deliverable:
FFN-ii: Location Aware System Deployment in the two Experimental Sites
The objective of this output is to provide information about the various tasks that took place during the 2nd cultivation period (Semesters V-VI) in the two experimental sites of the project aiming to verify the good performance of the e-traps, as well as to test the new functionalities and ensure the good operation of the e-services in the large-area sites. To accomplish the full deployment of the Location Aware (LA) products, the olive and Med e-traps were tested in both sites to satisfy all the evaluation criteria set for the full operation. Noteworthy, the OliveFlyNet was tested in Greece by the Agricultural University of Athens (AUA) and the MedFlyNet in Italy by the University of Molise (UNIMOL). The results for both types of the e-traps tested in the two experimental fields showed very good performance in terms of battery charge level and signal reception.
The e-services of adults’ population distribution map, infestation risk map and spray density map creation were tested by AUA in its experimental site to validate its functionalities in the new environment. A new e-service was developed and tested regarding the automatic digitalization of trees, that is an e-service which facilitates the implementation of the system. An additional e-service which gives the ability to the system to identify the optimum position for placing each trap in each area with olive groves was developed and tested too, so that to rationalize the approaches used. The two new e-services were implemented successfully whereas the updated software was able to produce all the different kind of maps required for the full implementation of the LA e-services.
Finally, a detection and count machine learning test for the case med fruit fly pest was also studied by UNIMOL. Tests related to the automatic recognition of medfly catches in the e-trap were also conducted and showed a very food response.
Keep Keywords:
Agriculture and fisheries and forestry ; Education and training ; Evaluation systems and results ; Green technologies ; Knowledge and technology transfer ; New products and services