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 aims to transform quantitative landscape metrics and animal data into practical tools and knowledge that grazing producers can use to measure animal welfare outcomes in extensive grazing systems. It employs data science and digital twin modelling to enhance both welfare and business outcomes through landscape stewardship.

Preferred candidate (skill set)

The preferred candidate should have a good animal science/veterinary background and be passionate about simulation models. Knowledge of programming and data science skills would be desirable. The application will be in an agricultural environment, particularly around livestock.

Supervisory team

Dr. Fendy Santoso
Professor Jane Quinn

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The Australian wool production forecasting traditionally relies on data from the Australian Bureau of Statistics (ABS), which conducts a comprehensive primary production survey every five years. This survey includes detailed information on livestock and crop numbers. However, the ABS has decided to discontinue this survey, leaving the wool industry without this crucial data. Despite this, there is consistent weekly data collected on livestock sales, slaughter numbers, and wool sales, including quality metrics.

By analysing historical data from Australian Wool Production Forecasting Committee (AWPFC) along with climatic conditions, satellite imagery of pasture information, and current weekly data this project will develop an integrated Australian Shorn Wool Production Forecasting System focusing on the following objectives.

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

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.

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