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.

As minimum area SRAM bit-cells are obtained when using cell ratio and pull-up ratio of 1, we analyze the possibility of decreasing the cell ratio from the conventional values comprised between 1.5-2.5 to 1. The impact of this option on area, power, performance and stability is analyzed showing that the most affected parameter is read stability, although this impact can be overcome using some of the read assist circuits proposed in the literature. The main benefits are layout regularity enhancement, with its consequent higher tolerance to variability, cell area reduction by 25 percent (with respect to a cell having a cell ratio of 2), leakage current improvement by a 35 percent, as well as energy dissipation reduction and a soft error rate per bit improvement of around 30 percent.

Introduction: Intelligent monitoring systems must be put in place to practice precision agriculture. In this context, computer vision and artificial intelligence techniques can be applied to monitor and prevent pests, such as that of the olive fly. These techniques are a tool to discover patterns and abnormalities in the data, which helps the early detection of pests and the prompt administration of corrective measures. However, there are significant challenges due to the lack of data to apply state of the art Deep Learning techniques.Methods: This article examines the detection and classification of the olive fly using the Random Forest and Support Vector Machine algorithms, as well as their application in an electronic trap version based on a Raspberry Pi B+ board.Results: The combination of the two methods is suggested to increase the accuracy of the classification results while working with a small training data set. Combining both techniques for olive fly detection yields an accuracy of 89.1%, which increases to 94.5% for SVM and 91.9% for RF when comparing all fly species to other insects.Discussion: This research results reports a successful implementation of ML in an electronic trap system for olive fly detection, providing valuable insights and benefits. The opportunities of using small IoT devices for image classification opens new possibilities, emphasizing the significance of ML in optimizing resource usage and enhancing privacy protection. As the system grows by increasing the number of electronic traps, more data will be available. Therefore, it holds the potential to further enhance accuracy by learning from multiple trap systems, making it a promising tool for effective and sustainable fly population management.

Modern agriculture requires technology to give precise measures about relevant parameters such as pest control. Here, we developed a decision support system (DSS) based on semi-automatic pest monitoring for managing the olive fruit fly Bactrocera oleae (Rossi), in Mallorca (Balearic Islands, Spain). The DSS was based on an algorithm that took into account spatial and temporal patterns of olive fruit fly population in an orchard where all trees were georeferenced, thus precise treatments against the pest were conducted through a location aware system (LAS). The olive fruit fly adult population was monitored by using ad hoc off-the-grid autonomous electronic traps.The results were compared with those obtained with conventional methods. For a pilot trial, we selected an olive-producing orchard, where from June to October 2015, three plots using LAS management and three plots under conventional control (NO-LAS plots) were compared. Spray threshold considered both adult population and fruit damage. An additional non-sprayed plot was selected for assessing biological control due to the parasitoid, Psyttalia concolor (Szépligeti). Results showed that the use of DSS reduced by 36.84% the volume of insecticide used in LAS compared to NO-LAS plots. Accordingly, time and distance needed for spraying were also reduced. Adult olive fruit fly population was lower in the LAS plots when compared with the NO-LAS plots; conversely, fruit infestation was higher in LAS compared with NO-LAS. The implementation of LAS and DSS at field level allowed real-time monitoring of adult olive flies, thereby increasing the accuracy and precision of sprays in time and space and decreasing impact on natural enemies.