Project topics

Project topics



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.


Digital soils and landscape

Supervisors: Luigi Renzullo (ANU), Craig Strong (ANU), Brendan Malone (CSIRO) and Mark Glover (CSIRO).

The interaction between static soil properties and dynamic climate and moisture data layers are foundational to agricultural productivity and sustainable landscape management. This PhD will explore ways in which to combine, fuse, assimilate and outscale existing and new sensing technologies across a range of scales. The project will leverage soil, drone, satellite and proximal digital data to build a powerful inference utility that can be applied for a diverse array of observation, prediction and forecasting contexts related to agricultural production systems. This could include soil moisture sensing, prediction and forecasting systems. Work would entail data layer integration of static environmental datasets and dynamic time-series datasets sourced from climate and remote and proximal sensing platforms. The project would require the use and development of terrain analytics, digital soil mapping and related geospatial analysis work pipelines.


Nutrient balance and Carbon trajectories (full description)

Supervisors: John Kirkegaard (CSIRO), Craig Strong (ANU), Alan Richardson (CSIRO), Kirsten Verburg (CSIRO), Martin Amidy (ANU) and Rachael Rodney Harris (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 systems level is 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.


Multivariate and machine learning statistical tools for linking crop multi-omics data

Supervisors: Shannon Dillon (CSIRO), Eric Stone (ANU/CSIRO), Barry Pogson (ANU) and Bob Furbank (ANU).

Advances in crop genomics and phenomics mean that it is becoming cheaper and easier to gather large sets of data from the genome, transcriptome, metabolome, proteome and physiological phenome of diverse germplasm. The power of these datasets to understand the genetic architecture of important crop traits is currently limited by analytical tools to drill through these diverse data layers and turn data into knowledge. This project seeks to build such multivariate statistical and machine learning tools using wheat datasets as a use case.


Detection and diagnosis of crop pathogens

Supervisors: Benjamin Schwessinger (ANU), Melania Figueroa (CSIRO), Robert Coe (CSIRO) and Eric Stone (ANU/CSIRO).

Crop pathogens cause significant yield losses in a wide variety of crops around the globe. The emergence and proliferation of plant diseases has been facilitated by increased human activities associated with agriculture. This includes changes to plant communities and native ecosystems as well as the establishment of dense monocultures. The tracking and monitoring of plant pathogens is a major challenge to ensure food production and quality. Real-time detection and rapid pathogen identification are essential components to safeguard agricultural and natural (eco) systems. This leads to increased biosecurity requirements, which is key to Australian agricultural exports. Furthermore, solutions for more sustainable agriculture demand precision crop protection strategies that effectively reduce the use of pesticides and fungicides to decrease ecological impacts.  Therefore, accurate and reliable in-field pathogen surveillance and diagnosis is essential for tailored and rapid responses and disease prevention. Recent breakthroughs in sensor-based methods through sequencing and phenotyping technologies combined with advances in Machine Learning and Artificial Intelligence offer opportunities to develop robust and mobile pathogen detection techniques as well as disease forecasting models. However, more research involving data acquisition, integration, analysis, and prediction is needed to capitalise on these opportunities. Through this CSIRO-ANU Digital Agriculture Supplementary Scholarship, a PhD student will pursue the development of point-of care diagnostic methods for plant pathogen detection in major crops while working closely with experts in disease and bioinformatics across CSIRO, Data61 and ANU.


Agricultural data fusion – earth observation

Supervisors: Luigi Renzullo (ANU) and Roger Lawes (CSIRO).

This project will investigate ways to utilise rapid advances in earth observation from a range of satellite data sources to solve science challenges in agricultural and landscape decision support. Increasingly, deep learning algorithms are being deployed in soil health, production estimates under climate risks, water management and natural capital accounting. 

This work will advance data science for improved management of modern data streams and their availability for application of novel computer learning approaches.


Sensing the next step-change in water productivity in dryland agriculture

Supervisors: John Kirkegaard (CSIRO), Craig Strong (ANU), Elizabeth Meier (CSIRO), Kirsten Verburg (CSIRO), and Julianne Lilley (CSIRO).

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 drought 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 processes 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. 

The PhD project will use farming systems analysis and modelling to better understand the benefits of real-time knowledge of plant-available water in on-farm decision making at the paddock and farm scale to improve the water productivity of dryland farms.  The project will develop an in-depth understanding of soil water use in cropping systems, and the impact of management decisions on water-use efficiency drawing on data from CSIRO’s Boorowa Agricultural Research Station (BARS) and other long-term cropping trials for verification and to augment the simulated data.


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. 


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.


Non-invasive plant growth monitoring using computer vision/AI techniques

Supervisors: Hongdong Li (ANU), Chuong Nguyen (CSIRO), Liang Zheng (ANU), and Tim Brown (ANU).

This research proposes to develop new, non-destructive methods to monitor plant growth and health to monitor plant growth using artificial intelligence (AI) and computer vision technologies. Currently, many measurements of plant health and growth (e.g., biomass, and leaf area, chemical constituents, leaf area, etc) require destructive harvesting and are often labour intensive. Destructive harvest requires larger sample sizes and is not suitable for time-series analysis of trait development in individual plants. The PhD candidate will support researchers from the ANU College of Engineering and Computer Science (CECS), the Australian Plant Phenomics Facility (APPF), and CSIRO DATA61 in developing new methods for image-based 3D reconstruction, pose estimation and creation of synthetic data and model training with synthetic data for 3D time-series plant phenotyping. Given the existing strengths in 3D modelling (CECS), non-invasive plant studies (Data61) and plant sciences (APPF), this cross-disciplinary research will directly benefit agricultural research by creating open source tools and workflows to tackle essential problems in plant sciences that have previously been intractable with more traditional methods. 



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