SunRice have developed an instrument called the PaddyVision®, which is the first of its kind in the world. The PaddyVision® non-destructively measures paddy rice quality at the point where it is received from a farmer and is a global advance in the real-time analysis of the potential milling quality of rice. This project is an excellent opportunity to be at the forefront of deep learning and machine learning while developing PaddyVision® as a global standard in rice quality measurement.

Preferred candidate (skill set):

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

Supervisory team:

Dr. Ibrar Yaqoob
Dr. Abdullah Abdullar
Dr. Jian Liu

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This project utilises publicly available information such as water allocations, dam inflows, historical rainfall, and crop competition, along with internal data including production volumes, yield, paddy prices, quality, consumer demand, sales, and milling capacity. Using this comprehensive data, the project develops advanced algorithms capable of generating predictive scenarios to help determine optimal commercial offerings, thereby maximising returns for growers and shareholders. Additionally, it creates sophisticated algorithms that segment and profile growers based on historical performance to forecast future outcomes. To enhance stakeholder engagement and decision-making, this project is also developing a digital twin system that provides easy visualisation and utilisation of these insights.

Preferred candidate (skill set):

A background in computer science/engineering or a demonstrated research record in AI/ML is highly desirable. Knowledge, experience, and passion for interdisciplinary research in agriculture, as well as digital twins, GIS and remote sensing are advantageous.

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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.

Preferred candidate (skill set):

A background in computer science or engineering or a proven research record in AI/ML is highly desirable. Knowledge, experience, and passion for interdisciplinary research in horticulture and environmental science, as well as digital twins and simulation models, are advantageous.

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This project will develop a digital twin platform that leverages real operational data captured from a hydrogen production system to optimise agricultural hydrogen utilisation across multiple sectors. By integrating hydrogen production data, energy consumption patterns, and agricultural operational requirements, this platform will create detailed virtual models that simulate optimal hydrogen deployment strategies for agricultural applications such as livestock operations, vineyards, feedlots, but not limited to these. The digital twin will model various hydrogen utilisation scenarios, production scheduling, and distribution strategies to maximise efficiency and return on investment while ensuring cyber-secure data management and system resilience.

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.

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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 to build models on environmental and geospatial data to determine a decision support framework for variable rate application of pre-emergent herbicides. Using a digital twin virtual farming system aggregates field topography, soil parameters, previous operations and satellite imagery to create zones and scenarios that assess best economical and productive rate of herbicides, reducing crop injury and potentially increasing weed control efficacy. This approach determines the bio-economically optimum herbicide rate for specific zones within paddocks while adhering to registered label rates.

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.

Supervisory team:

Dr. Fendy Santoso

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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.

Supervisor Team:

Dr. Ivan Maksymov (Primary supervisor),
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.

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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