HDR Scholarship - Sensing and data analytics for quantifying stress and traits in grain crops

Remote sensing has been used in agriculture since at least the 1970s to map crops and detect and measure stresses for yield forecasting. This technology has advanced from broadband, low resolution multispectral to narrow band hyperspectral data and imagery – along with thermal, fluorescence, and LiDAR sensing. The ability of modern sensors to detect a range of stresses important to improving crop adaptation to more extreme environments and changing climate has advanced dramatically. In parallel, data analytical techniques, such as machine learning and broadly, artificial intelligence, have increased our ability to detect and quantify stress signals and patterns within increasingly large data sets. These technologies are increasingly being applied to crops to quantify abiotic and biotic stress impacts to improve yield predictions, forecast grain quality, and characterize important traits for high throughput crop improvement.

Research conducted on the Agriculture Victoria Research (AVR) SmartFarm in Horsham includes a range of crops grown in the Wimmera, a semi-arid dryland region of southeastern Australia. The predominant crops include wheat, barley, canola and pulses, such as lentil, peas, and chickpeas. Research includes studying the impacts of heat, drought, frost and disease to crops, selection of traits for crop improvement and soil and water impacts to crops. Experiments are conducted both on a research farm and in grower’s fields. In addition, AVR has available a range of ground and aerial sensing technologies including ground-based hyperspectral, thermal and sun-induced fluorescence sensors, Remotely Piloted Aerial (RPA) multispectral, thermal and LiDAR sensors. These are available for the successful student to conduct their research project. This candidature period includes a compulsory Industry Placement period of six months. The student is expected to continue to work predominantly on the degree related research/thesis during the placement period.The PhD candidate would be spending at least 6 months at the Horsham SmartFarm, conducting field work and collecting data for their project.

Applications close 5pm, Friday 17 January 2025

This scholarship is available over 3 years.

• Stipend of $41,650 per annum tax exempt

To be eligible you must:

• be a domestic candidate. Domestic includes candidates with Australian Citizenship, Australian Permanent Residency or New Zealand Citizenship.
• meet Deakin's PhD entry requirements
• be enrolling full time and hold an honours degree (first class) or an equivalent standard master's degree with a substantial research component.
• not be in full time employment at time of commencement of scholarship;
• meet the requirements for CSIRO Student affiliate onboarding (e.g. satisfy National Police Check);
• not be subject to an obligation to a third party to provide that third party with rights to any Intellectual Property created in the course of their degree; and

Please refer to the research degree entry pathways page for further information.

Additional desirable criteria include:

• Programming skills in remote sensing and GIS software such as Python, R, ENVI, and/or QGIS, and interest in field deployment of sensors.
• The ability to integrate hardware and analytical capability with understanding the biology of crop growth and development within the disciplines of precision agriculture and crop breeding