Developing Image Datasets and Recognition Algorithms for Major Weeds in Queensland Cropping Systems

Weeds remain a significant threat to crop production in Queensland, with herbicide resistance emerging as an escalating challenge. Precision agriculture technologies offer promising solutions for effective, site-specific weed control while reducing herbicide use and mitigating the environmental impacts of indiscriminate application. However, the development of these technologies is constrained by the lack of diverse, well-labeled image datasets (for weeds and cash crops) necessary for training robust weed recognition algorithms. The specific objectives are to:

1. develop a diverse, well-annotated image dataset of weeds and cash crops, following established collection and curation protocols;
2. train and test a machine learning model for detecting Paradoxa grass in wheat systems;
3. develop and test a detection model for feathertop Rhodes grass in sorghum and mungbean; and
4. disseminate findings to stakeholders through targeted outreach and communication platforms.

The candidates will join Prof. Chauhan’s Weed Research program, which currently comprises two postdocs, two PhD and four MS students. Chauhan’s lab possesses a track record of sustained research excellence, as evidenced by its high number of publications indexed in Scopus. The candidate's project is aligned with a GRDC-funded initiative, "Weed management initiative", with all research costs for the PhD covered by this project. QAAFI will provide office desks, laboratory facilities, and shade house space at Gatton. The candidate will be actively encouraged to participate in project meetings, seminars, project planning sessions, farmers' updates, and regular meetings with fellow students. QAAFI students are eligible for conference travel awards. Additionally, the candidate will travel to growers' fields periodically with the project team to meet with growers and other industry stakeholders. Furthermore, the candidate will attend project meetings with all national partners, including the funding body, GRDC.

This project is supported by the Research project scholarship.

Learn more about the Research project scholarship.

Your application will be assessed on a competitive basis.

We take into account your:

• previous academic record
• publication record
• honours and awards
• employment history

A strong interest in applying digital technologies to advance sustainable weed management would be of benefit to someone working on this project.

You'll demonstrate an ability to learn and apply new concepts and technical skills rapidly and effectively. 

A background or knowledge of remote sensing, sensor technologies/precision agriculture, combined with agronomy knowledge will be advantageous.

You must submit an expression of interest (EOI) by 12 January, 2026 12 January, 2026.

Before you apply

1. Check your eligibility for the Doctor of Philosophy (PhD).
2. Prepare your documentation.
3. If you have any questions about whether the project is suitable for your research interests, contact Professor Bhagirath Chauhan (b.chauhan@uq.edu.au).

When you apply

To apply, submit an expression of interest (EOI) for the program. You don't need to apply separately for the project or scholarship. How to submit an EOI

In your EOI, complete the ‘Scholarship/Sponsorship’ section with the following details:

1. Are you applying for an advertised project: 'Yes'
2. Project: 'Research project scholarship'
3. Scholarship Code Listed in the Advertisement: WEEDS-CHAUHAN
4. Link to Scholarship Advertisement: https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/developing-image-datasets-and-recognition-algorithms-major-weeds-queensland-cropping-systems

Submit an EOI