Novel paramyxoviruses in Australian flying-fox populations support host–virus co-evolution

Understanding the diversity of henipaviruses and related viruses is important in determining the viral ecology within flying-fox populations and assessing the potential threat posed by these agents. This study sought to identify the abundance and diversity of previously unknown paramyxoviruses (UPVs) in Australian flying-fox species (Pteropus alecto, Pteropus scapulatus, Pteropus poliocephalus and Pteropus conspicillatus) and in the Christmas Island species Pteropus melanotus natalis. Using a degenerative reverse transcription-PCR specific for the L gene of known species of the genus Henipavirus and two closely related paramyxovirus genera Respirovirus and Morbillivirus, we identified an abundance and diversity of previously UPVs, with a representative 31 UPVs clustering in eight distinct groups (100 UPVs/495 samples). No new henipaviruses were identified. The findings were consistent with a hypothesis of co-evolution of paramyxoviruses and their flying-fox hosts. Quantification of the degree of co-speciation between host and virus (beyond the scope of this study) would strengthen this hypothesis.

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.