During the last decades, the economic importance of tephritid fruit flies (FF) has increased worldwide because of recurrent invasions and expansions into new areas, and reduced control capabilities of current control systems. Efficient monitoring systems, thus, are required to provide fast information to act promptly. With this aim in mind, we developed two electronic trap (e‐trap) versions for adult FF: one with specific volatiles for male and female adult Ceratitis capitata, and the second, based on the attraction of adult FF to yellow colour, targeting Dacus ciliatus, Rhagoletis cerasi and Bactrocera oleae. In the case of B. oleae, the female pheromone and ammonium bicarbonate were added as synergists. In the two versions, attracted FF were retained in the trap on glued surfaces. Real‐time images of the surfaces were automatically taken and transmitted to a server. We tested the two e‐trap versions in insect‐proof cages, where flies were released and recaptured, and in commercial orchards throughout the Mediterranean: C. capitata in peach orchards in Italy; R. cerasi in cherry orchards in Greece; B. oleae in olive orchards in Spain and in Greece; and D. ciliatus in melons in plastic tunnels in Israel. The e‐trap showed excellent abilities to transmit real‐time images of trapped FF and a high specificity for trapping different FF species. The ability of the entomologist to correctly classify FF from images in the office was >88%. In addition, average number of flies/trap in e‐trap grids did not differ from numbers reported on grids of conventional traps that were operating simultaneously. The e‐traps developed and tested in this study provide the basis for the real‐time monitoring of FF were no olfactory attractants are available, and for the surveillance of alien FF incursions where generic, but not specific, olfactory attractants exists.

A Decision Support System (DSS) was developed and evaluated to control the Mediterranean fruit fly (medfly), Ceratitis capitata (Wiedermann), by incorporating a semi-automatic pest monitoring and a precision targeting approach in multi-varietal orchards. The DSS consists of three algorithms. DSS1, based on the degree days calculation, defines when the traps should be deployed in the field initiating the medfly population monitoring. DSS2 defines the areas to be treated and the type of treatment based on the number of adult medfly captures, harvesting time, and phenological stage of the host cultivar. DSS3 defines the spraying procedure considering the technical registration properties of the selected insecticide (e.g., withholding period and efficacy duration time) and weather conditions. The DSS was tested in commercial orchard conditions near Rome, central Italy, with a randomized complete blocks experimental design, comparing DSS-assisted and conventional management. In the DSS-assisted plots, a semi-automatic adult medfly monitoring system was deployed, composed of real-time, wireless electronic traps. The output of the functioning DSS is a map of spraying recommendation, reporting the areas to be treated and the treatment type (bait or cover insecticide spraying). The farmer was left free to follow, or not, the DSS indications. The first medfly captures were observed on June 30, whereas the DD threshold was reached on July 3 when the DSS started to operate. The field test produced 29 DSS decisions from July 3 to September 1 and confirmed that medfly management using the DSS substantially reduced the number of pesticide applications, the treated area, and the volumes of pesticide utilization. No significant differences in infested fruit were observed between DSS-assisted and conventional management. The level of acceptance of the DSS by the farmer was 78%. This evidence confirmed the requirement of fully involving farmers and pest managers during the evaluation process of DSS.