Project topics
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Novel phenomics tools to screen for drought tolerance in wheat
Supervisors: Bob Furbank (ANU), Fernanda Dreccer (CSIRO) and Rob Coe (CSIRO).
Drought is a major environmental factor reducing wheat yields across Australia. Drought tolerance is a complex multigenic trait and difficult to assess rapidly in the glasshouse or the field. This project seeks to develop new methods based on the hyperspectral reflectance, the spectrum light reflected from the wheat leaves and canopy, by using machine learning to build predictive algorithms for selection of drought tolerant wheat in the field. These tools can be used both to find superior germplasm and to understand the physiological basis of drought tolerance mechanisms at key stages of crop development.
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Nutrient balance strategies to build soil carbon in farming systems (full description)
Supervisors: Julianne Lilley (CSIRO), John Kirkegaard (CSIRO), Alan Richardson (CSIRO), Kirsten Verburg (CSIRO), Craig Strong (ANU), Rachael Rodney Harris (ANU) and Martin Amidy (ANU).
Crop nutrition in agriculture is a complex interplay of crop physiology and timing and availability of nutrients, with both demand and supply affected by climate, soil, and management. Its study requires consideration of a multi-layered system in time and space. We suspect that insufficient crop nutrition (nitrogen in particular) is the biggest cause of the current “yield gap” in dryland agriculture (difference between actual and potential yield). At the same time, we know that soil “nutrient mining” through inadequate nutrition drives the loss of carbon (organic matter) in soil. Fine tuning and de-risking our nutrient management to maintain a nutrient balance at the system’s level is the key to dealing with these two critical but linked national goals. More deeply understanding the nutrient balance will have impacts not only towards crop productivity, but also carbon accounting and environmental impacts on soils, water, and landscapes.
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Plant Architecture Mapping in the Lab and in the Field
Supervisors: Tim Brown (ANU), Matt Adcock (CSIRO), Rob Coe (CSIRO) and Chuong Nguyen (CSIRO).
In digital agriculture, we now have the ability to scan plants in the lab and field at millimeter resolution. However, mapping these data to real plant architecture is still a largely unsolved problem. Pose estimation, deformable models, GANs and Neural Radiance Fields have yet to be applied widely to tackle this challenge. This project will explore ways to extend and adapt techniques that have been effective in controlled conditions to the challenges of plant architecture acquisition out in the field.
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Developing a low-cost digital crop monitoring system
Supervisors: Warren Jin (CSIRO), Rose Broderick (CSIRO) and Owen Atkin (ANU).
For farming operation, farmers or their consultants are keen to know very detailed information about their crops and farms, such as the damaged areas of frost event last night, or which part or when to irrigate in upcoming days. A low-cost crop monitoring system is valuable to Agriculture in Australia. Economic losses due to crop damage caused by frost in Australia are estimated to be many hundreds of millions of dollars (An-Vo et al., 2018; Fletcher et al., 2019; March et al., 2015). Overuse and poor irrigation practices may waste precious water, energy and money, reduce quality or yield, and can lead to leaching or run-off of nutrients that can pollute groundwater, rivers and estuaries. In reality, irrigated agriculture accounts for approximately 90% of water use in the world, and between 1950 and 2006, food production doubled, while agricultural water consumption tripled (O’Donnell, et al 2019). However, it is often not cheap to collect these kinds of on-farm details in a cost-effective way. For example, on one hand, thermal images from drones/helicopters/satellites can easily cover a large region but normally with low temporal granularity and/or low spatial resolution. On the other hand, in-situ sensors can collect point-based continuous time-series observations but too expensive to cover a large area. This PhD project will develop fusion techniques to combine low frequency image observation with high-frequency point-specific time series to provide low-cost high-frequency information about crops. Some intelligent computation techniques will be explored, such as low-rank approximation, local kriging, spatially-varying coefficient modelling, and periodic modelling, which has been used by CSIRO to demonstrate their potentials (Shao et al. 2020, Bakar et al. 2016, Jin et al 2021). These techniques will be demonstrated on applications such as monitoring frost damage and crop irrigation scheduling for case study regions.
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Sensing the next step change in water productivity in dryland agriculture (full description)
Supervisors: John Kirkegaard (CSIRO), Julianne Lilley (CSIRO), Elizabeth Meier (CSIRO), Kirsten Verburg (CSIRO), Craig Strong (ANU) and Rachael Rodney Harris (ANU).
Better knowledge of soil water content at paddock and farm scale underpins improvements to almost all decisions facing agriculture in an increasingly variable climate. If, when and what crops to sow, how much fertiliser or other inputs to use, whether to cut crops for hay or grow on for grain in droughts and even yield predictions for marketing purposes are all decisions that can be influenced by a more accurate measurement of the water available to plants in the soil. Historically, soil water availability was estimated from rainfall data using simple calculators and local knowledge of the soil, but new soil water sensing technology is making it increasingly possible to measure soil water in real time. Wireless soil water sensors that can monitor soil water in real time to 2m depth are now a reality, but the value of that information in the context of variable seasonal outlooks and process is uncertain. In terms of de-risking management and input decisions, knowing how much water is in the soil and available to plants together with seasonal forecasts could be transformational. De-risking management decisions is the key to improve the productivity and water-use efficiency of our dryland systems and reduce the environmental impacts on soils, water, and landscapes.
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