This project aims to create a comprehensive digital twin of the Sunrice rice handling and processing facility to enhance operational efficiency, optimise resource utilisation, and improve product quality and sustainability. By leveraging historical data from the facility, the project will develop machine learning algorithms to build a digital twin, enabling detailed analysis, visualisation, and monitoring of rice processing steps.

Preferred candidate (skill set):
The preferred candidate should have strong programming and data science skills. Knowledge of digital twins and IoT will be desirable, and the application will be in agriculture.

Supervisory team:
Dr. Ibrar Yaqoob
Dr. Abdullah Abdullar
Dr. Jian Liu

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Plant simulation models such as APSIM have been developed for simulating plant growth in recent years. However, decision-making support specifically for rice production remains a significant gap. This project will leverage emerging technologies, particularly machine learning and digital twins, along with geographic information systems (GIS), remote sensing, and data analytics, to provide comprehensive decision-making support for rice farmers, policymakers, and other stakeholders. By integrating geospatial data, grain yield, and quality metrics with advanced analytics, the system will enable predictive models that link GIS data and sensing data to forecast rice growth, grain quality, and inform decisions on water and fertilisation management.

Collaboration:  This project is being delivered in collaboration with University of Sydney.

This project explores advanced 3D simulation technology to enhance the management and productivity of the vineyard. The project aims to simulate various management scenarios and evaluate their impacts, by constructing a comprehensive virtual model that encompasses the vineyard’s topography, soil properties, plant variety, and environmental conditions. This model will enable functions to predict the outcomes of different irrigation schedules, nutrient applications, and pest control measures on vine growth, fruit yield, and overall vineyard health.

Collaboration:  This project is being delivered in collaboration with University of New South Wales.

This project is in design phase with industry. Project details and Expressions of Interest will be available shortly.

This project aims to apply digital twin technology to map and monitor land use in the dairy production region, focusing on greenhouse gas (GHG) monitoring and enhancing environmental sustainability. Utilising data from satellite imagery, drones, sensors, and weather stations, the digital/quantum twin will integrate land topography parameters, soil health data, and microclimate data to provide a dynamic and comprehensive representation of the region. This approach will enable better decision-making and environmental stewardship in dairy production, GHG monitoring, grazing management, enhancing both productivity and sustainability.

Collaboration:  This project is being delivered in collaboration with University of Queensland.

Microalgae and cyanobacteria are promising bioresources for food and plant bio-stimulant products. Algenie developed a novel "helical" photobioreactor for growing microalgae and is hyper-focused on getting production costs as low as possible. A robust feedback control system will be implemented to rapidly identify the ideal growing environments for any species being tested and determine the optimal conditions for growth. This project will utilise a Digital Twin platform, which simulates the entire cultivation process in a virtual environment, aiming to expedite the overall workflow significantly, allowing us to iterate through experimental designs and optimise outputs rapidly.

Understanding spatial variability in agricultural landscapes is essential for efficiently simulating production and developing digital decision support tools. This honours project focuses on soil moisture and its relationship with plant growth and nutrient uptake, which are critical for improved crop management.
The project will utilise an extensive network of soil moisture probes located across different soil zones and adjacent management areas at the Charles Sturt University Dhulura farm. The project will investigate the variability of soil water changes and their impact on plant growth nearby. Particular attention will be given to the observed differences in measured characteristics and how these align with established crop growth models that are usually applied at less intensive spatial scales. Gaining a deeper understanding of these relationships will be vital for future efforts to develop integrated spatially predictive models.

Preferred candidate (skill set):
The preferred candidate should have good programming and data science skills and be ready to enrol in an honours course at Charles Sturt University.

Supervisory team:
Professor Ganna Pogrebna
Dr. Jason Smith

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This project is in design phase with industry. Project details and Expressions of Interest will be available shortly.

This project is in design phase with industry. Project details and Expressions of Interest will be available shortly.

This innovative PhD project will develop a data-driven digital platform to support the forecast Australia’s shorn wool production. By integrating geospatial data (pasture growth, soil moisture, climate records) with industry data (livestock slaughter figures, wool test and  wool auction volumes), the project aims to build a robust system that separates the impact of seasonal variation from changes in flock size and per head production. The system will provide  quantitative forecasts of shorn wool production, but also insights into the quality profile of the Australian wool clip, helping producers, processors, and buyers make more informed decisions.

Key Research Activities:

• Develop tools to assess pasture growth trends over time using satellite imagery and climate data, at Local Government Area (LGA) scale.
• Build and validate statistical and machine learning models using real-world data from ABS, AWTA, AWEX, and other sources.
• Integrate economic signals (e.g. wool prices, input costs) to understand how market dynamics influence production decisions.
• Work with Makin Outcomes and collaborate with stakeholders across the wool value chain to ensure the system meets real-world decision-making needs.

Preferred candidate (skill set)

Applicants with backgrounds in agricultural science, remote sensing, environmental science, data analytics, economics, or systems modelling are encouraged to apply. Prior experience with coding (e.g. Python, R), GIS, or statistical modelling will be highly regarded.

Supervisor Team:

Dr. Ivan Maksymov (Primary supervisor),
Prof. Ganna Pogrebna,
Dr. Sue Hatcher,
Mr Jonathan Medway.

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The Cool Soil Initiative (CSI) has already collected paddock-level data across 190 farms over a period of up to 5 years, including farm practices such as pulse crops, pasture rotation, fertilisation application, soil health parameters, and economic data. This project will integrate these datasets into a digital twin prototype model using the historic data from the current Global Smart Farm Network.  This model will be enabled to undertake relational analysis of datasets to understand current climate-smart agricultural innovations and the drivers of resilience in the major Australian cropping region to:

• Increase the efficiency of farm inputs.
• Maximise system benefits and yield.
• Mitigate risks in extreme seasons.

Preferred Candidate skills:

A background in computer science/engineering or a demonstrated research record in AI/ML is highly desirable. Knowledge of soil and environmental science, digital twins, GIS, remote sensing, and IoT is advantageous.

Supervisor Team:

Dr. Ibrar Yaqoob (Primary Supervisor)
Dr. Abdullah (Co-Supervisor)
Matthew Muller (Industry Consultant)

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The Charles Sturt University Global Digital Farm is a 2,500-hectare farming enterprise consisting of two properties in Wagga Wagga and Orange, supporting a diverse range of agricultural production, teaching, and research activities.

This project will investigate the spatial and temporal relationships among various soil, plant, and weather data, as well as crop management practices within the dryland cropping program. A range of existing crop and plant growth models (APSIM, Yield Prophet, etc.) that utilise these data and relationships will then be integrated to create a spatially enabled, digital twin-based decision support system focused initially on fertiliser management of cereals. With other management issues, crop types, and pastures also potentially able to utilise a similarly structured capability, an emphasis on developing a template-based framework will be employed to streamline future applications.

Preferred candidate (skill set):
The preferred candidate should have strong programming and data science skills. Knowledge of agricultural/environmental science, digital twins, GIS, remote sensing, and IoT will be desirable, and the application will be in the agricultural environment.

Supervisory team:
Professor Ganna Pogrebna
Professor Geoff Gurr
Mr. Jonathan Medway etc.

Express your interest

This project is in design phase with industry. Project details and Expressions of Interest will be available shortly.

This project is in design phase with industry. Project details and Expressions of Interest will be available shortly.

This project aims utilise quantitative biometric and animal data into practical tools to assist livestock producers in detecting, monitoring, and rapidly intervening rapidly to improve and enhance animal productivity and welfare in feedlots and yards, utilising vision AI and data science techniques. The project will utilise animal data to develop vision transfer-based AI models to identify key features that have the most significant influence on animal condition and welfare metrics, such as BRD, pink eye, lameness, aggressive interactions, injuries, stress, movement, weight change, and shy feeding, among others. Subsequently, it will develop vision AI models of feedlot livestock that can simulate the impacts of livestock management interventions on animal welfare and production outcomes to inform the cost-benefit analysis of intervention strategies.

Preferred candidate (skill set):

The preferred candidate should have a background in computer science or engineering, or good knowledge of machine learning methods and the Python programming language. An MPhil, Master’s by research, or Honours degree is essential or a demonstrated research record. Knowledge of animal science, digital twins, IoT will be advantageous.

Supervisory team:

Dr. Mohammad Ali Moni

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