Job no: 505386
Work type: Fixed Term
Location: Canberra / ACT
Classification: Academic Level A or Academic Level B
Salary package: Level A $66,318 – $84,122; Level B $91,541 – $104,254 plus up to 17% Superannuation
Fixed Term: 24 Months
Applications close: 19 September 2015
Seeking a skilled computer vision specialist interested in developing applied solutions for high-throughput analysis of plant phenotypes. The researcher will work in the intersection of Computer Vision and Plant Sciences within the collaboration between Centre of Excellence for Robotics Vision (ACRV, roboticvision.org), Centre of Excellence for Plant Energy Biology (CPEB, plantenergy.uwa.edu.au), and the Australian Plant Phenomics Facility (APPF; plantphenomics.org.au). This is a unique opportunity to participate in one of Australia’s strategic initiatives to strengthen and complement current activities in both Computer Vision and Plant Biology with the goal of applying the latest developments in Computer Science to better understand plant and crop development in changing climates.
The Postdoctoral Fellow/ Research Fellow will contribute to existing School research projects by providing continued development of the experimental facilities and techniques. Research challenges include management of large datasets for real-time segmentation and analysis of 400,000 plant images/day in changing lighting conditions with National Computational Infrastructure (NCI) VM’s and supercomputer resources. Extensions of the primary pipeline into additional areas of research are encouraged based on the applicant’s interests. More information
Boris Parent, Fahimeh Shahinnia, Lance Maphosa, Bettina Berger, Huwaida Rabie, Ken Chalmers, Alex Kovalchuk, Peter Langridge and Delphine Fleury (2015). Journal of ExperimentalBotany. doi:10.1093/jxb/erv320
Crop yield in low-rainfall environments is a complex trait under multigenic control that shows significant genotype x environment (GxE) interaction. One way to understand and track this trait is to link physiological studies to genetics by using imaging platforms to phenotype large segregating populations. A wheat population developed from parental lines contrasting in their mechanisms of yield maintenance under water deficit was studied in both an imaging platform and in the field. We combined phenotyping methods in a common analysis pipeline to estimate biomass and leaf area from images and then inferred growth and relative growth rate, transpiration, and water-use efficiency, and applied these to genetic analysis. From the 20 quantitative trait loci (QTLs) found for several traits in the platform, some showed strong effects, accounting for between 26 and 43% of the variation on chromosomes 1A and 1B, indicating that the G×E interaction could be reduced in a controlled environment and by using dynamic variables.
Co-location of QTLs identified in the platform and in the field showed a possible common genetic basis at some loci. Co-located QTLs were found for average growth rate, leaf expansion rate, transpiration rate, and water-use efficiency from the platform with yield, spike number, grain weight, grain number, and harvest index in the field.
These results demonstrated that imaging platforms are a suitable alternative to field-based screening and may be used to phenotype recombinant lines for positional cloning. Full PDF