The plume also rises: trajectories of pheromone plumes issuing from point sources in an orchard canopy at night

The trajectories of pheromone plumes in canopied habitats, such as orchards, have been little studied. We documented the capture of male navel orangeworm moths, Amyelois transitella, in female-baited traps positioned at 5 levels, from ground level to the canopy top, at approximately 6 m above ground, in almond orchards. Males were captured in similar proportions at all levels, suggesting that they do not favor a particular height during ranging flight. A 3-D sonic anemometer was used to establish patterns of wind flow and temperature at 6 heights from 2.08 to 6.65 m in an almond orchard with a 5 m high canopy, every 3 h over 72 h. The horizontal velocity of wind flow was highest above the canopy, where its directionality also was the most consistent. During the time of A. transitella mating (0300–0600), there was a net vertical displacement upward. Vertical buoyancy combined with only minor reductions in the distance that plumes will travel in the lower compared to the upper canopy suggest that the optimal height for release of pheromone from high-release-rate sources, such as aerosol dispensers (“puffers”), that are deployed at low densities (e.g., 3 per ha.) would be at mid or low in the canopy, thereby facilitating dispersion of disruptant throughout the canopy. Optimal placement of aerosol dispensers will vary with the behavioral ecology of the target pest; however, our results suggest that current protocols, which generally propose dispenser placement in the upper third of the canopy, should be reevaluated.

Instantaneous threat detection based on a semantic representation of activities, zones and trajectories

Threat detection is a challenging problem, because threats appear in many variations and differences to normal behaviour can be very subtle. In this paper, we consider threats on a parking lot, where theft of a truck’s cargo occurs. The threats range from explicit, e.g. a person attacking the truck driver, to implicit, e.g. somebody loitering and then fiddling with the exterior of the truck in order to open it. Our goal is a system that is able to recognize a threat instantaneously as they develop. Typical observables of the threats are a person’s activity, presence in a particular zone and the trajectory. The novelty of this paper is an encoding of these threat observables in a semantic, intermediate-level representation, based on low-level visual features that have no intrinsic semantic meaning themselves. The aim of this representation was to bridge the semantic gap between the low-level tracks and motion and the higher-level notion of threats. In our experiments, we demonstrate that our semantic representation is more descriptive for threat detection than directly using low-level features. We find that a person’s activities are the most important elements of this semantic representation, followed by the person’s trajectory. The proposed threat detection system is very accurate: 96.6 % of the tracks are correctly interpreted, when considering the temporal context.