A mitochondrial species identification assay for Australian blacktip sharks (Carcharhinus tilstoni, C. limbatus and C. amblyrhynchoides) using real-time PCR and high-resolution melt analysis

Tropical Australian shark fisheries target two morphologically indistinguishable blacktip sharks, the Australian blacktip (Carcharhinus tilstoni) and the common blacktip (C. limbatus). Their relative contributions to northern and eastern Australian coastal fisheries are unclear because of species identification difficulties. The two species differ in their number of precaudal vertebrae, which is difficult and time consuming to obtain in the field. But, the two species can be distinguished genetically with diagnostic mutations in their mitochondrial DNA ND4 gene. A third closely related sister species, the graceful shark C. amblyrhynchoides, can also be distinguished by species-specific mutations in this gene. DNA sequencing is an effective diagnostic tool, but is relatively expensive and time consuming. In contrast, real-time high-resolution melt (HRM) PCR assays are rapid and relatively inexpensive. These assays amplify regions of DNA with species-specific genetic mutations that result in PCR products with unique melt profiles. A real-time HRM PCR species-diagnostic assay (RT-HRM-PCR) has been developed based on the mtDNA ND4 gene for rapid typing of C. tilstoni, C. limbatus and C. amblyrhynchoides. The assay was developed using ND4 sequences from 66 C. tilstoni, 33. C. limbatus and five C. amblyrhynchoides collected from Indonesia and Australian states and territories; Western Australia, the Northern Territory, Queensland and New South Wales. The assay was shown to be 100% accurate on 160 unknown blacktip shark tissue samples by full mtDNA ND4 sequencing.

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.