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.
The Ethiopian fruit fly (EFF), Dacus ciliatus, is a key, invasive pest of melons in the Middle East. We developed and implemented a novel decision support system (DSS) to manage this pest in a greenhouse environment in Southern Israel. Dacus ciliatus is commonly controlled in Israel with repeated calendar-sprayings (every 15 days) of pyrethroid pesticides. The current study compares the performance of a DSS against calendar-spraying management (CSM). DSS was based on EFF population monitoring and infestation. DSS took into consideration concerns and observations of expert managers and farmers. During 2014, EFF damage was concentrated in the spring melon production season. Fall and winter production did not show important damage. Damage during the spring of 2014 started to increase when average EFF/trap/day reached 0.3. This value was suggested as the threshold to implement pesticide spraying in DSS greenhouses. EFF/trap/day trends were derived from monitoring with conventional traps and a novel electronic remote sensing trap, developed by our group. CSM during the spring of 2015 included 3 EFF control sprays, while DSS-managed greenhouses were only sprayed once. At the end of the spring season, damage was slightly higher in DSS greenhouses (1.5%), but not significantly different to that found in CSM greenhouses (0.5%). Results support continuing DSS research and optimization to reduce/remove pesticide use against EFF in melon greenhouses. Interactions with farmers and managers is suggested as essential to increase adoption of DSS in agriculture.
Timely detection of an invasion event, or a pest outbreak, is an extremely challenging operation of major importance for implementing management action toward eradication and/or containment. Fruit flies—FF—(Diptera: Tephritidae) comprise important invasive and quarantine species that threaten the world fruit and vegetables production. The current manuscript introduces a recently developed McPhail-type electronic trap (e-trap) and provides data on its field performance to surveil three major invasive FF (Ceratitis capitata, Bactrocera dorsalis and B. zonata). Using FF male lures, the e-trap attracts the flies and retains them on a sticky surface placed in the internal part of the trap. The e-trap captures frames of the trapped adults and automatically uploads the images to the remote server for identification conducted on a novel algorithm involving deep learning. Both the e-trap and the developed code were tested in the field in Greece, Austria, Italy, South Africa and Israel. The FF classification code was initially trained using a machine-learning algorithm and FF images derived from laboratory colonies of two of the species (C. capitata and B. zonata). Field tests were then conducted to investigate the electronic, communication and attractive performance of the e-trap, and the model accuracy to classify FFs. Our results demonstrated a relatively good communication, electronic performance and trapping efficacy of the e-trap. The classification model provided average precision results (93–95%) for the three target FFs from images uploaded remotely from e-traps deployed in field conditions. The developed and field tested e-trap system complies with the suggested attributes required for an advanced camera-based smart-trap.
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