The mating system of the true fruit flyBactrocera tryoniand its sister-species, Bactrocera neohumeralis

The frugivorous “true” fruit fly, Bactrocera tryoni (Queensland fruit fly), is presumed to have a nonresourced-based lek mating system. This is largely untested, and contrary data exists to suggest Bactrocera tryoni may have a resource-based mating system focused on fruiting host plants. We tested the mating system of Bactrocera tryoni, and its close sibling Bactrocera neohumeralis, in large field cages using laboratory reared flies. We used observational experiments that allowed us to determine if: (i) mating pairs were aggregated or nonaggregated; (ii) mating system was resource or nonresource based; (iii) flies utilized possible landmarks (tall trees over short) as mate-rendezvous sites; and (iv) males called females from male-dominated leks. We recorded nearly 250 Bactrocera tryoni mating pairs across all experiments, revealing that: (i) mating pairs were aggregated; (ii) mating nearly always occurred in tall trees over short; (iii) mating was nonresource based; and (iv) that males and females arrived at the mate-rendezvous site together with no evidence that males preceded females. Bactrocera neohumeralis copulations were much more infrequent (only 30 mating pairs in total), but for those pairs there was a similar preference for tall trees and no evidence of a resource-based mating system. Some aspects of Bactrocera tryoni mating behavior align with theoretical expectations of a lekking system, but others do not. Until evidence for unequivocal female choice can be provided (as predicted under a true lek), the mating system of Bactrocera tryoni is best described as a nonresource based, aggregation system for which we also have evidence that land-marking may be involved.

New developments in UAV remote sensing for pest management and the implications for cotton

Efficient crop monitoring and pest damage assessments are key to protecting the Australian agricultural industry and ensuring its leading position internationally. An important element in pest detection is gathering reliable crop data frequently and integrating analysis tools for decision making. Unmanned aerial systems are emerging as a cost-effective solution to a number of precision agriculture challenges. An important advantage of this technology is it provides a non-invasive aerial sensor platform to accurately monitor broad acre crops. In this presentation, we will give an overview on how unmanned aerial systems and machine learning can be combined to address crop protection challenges. A recent 2015 study on insect damage in sorghum will illustrate the effectiveness of this methodology. A UAV platform equipped with a high-resolution camera was deployed to autonomously perform a flight pattern over the target area. We describe the image processing pipeline implemented to create a georeferenced orthoimage and visualize the spatial distribution of the damage. An image analysis tool has been developed to minimize human input requirements. The computer program is based on a machine learning algorithm that automatically creates a meaningful partition of the image into clusters. Results show the algorithm delivers decision boundaries that accurately classify the field into crop health levels. The methodology presented in this paper represents a venue for further research towards automated crop protection assessments in the cotton industry, with applications in detecting, quantifying and monitoring the presence of mealybugs, mites and aphid pests.