PhD Degree in machine vision technologies for Controlled Environment Agriculture

This project will advance automated technologies for machine vision sensing of plant health in Controlled Environment Agriculture (CEA), for example in vertical farming systems. CEA is projected to be of critical importance to meet food production targets on Earth to address finite land availability and a growing global population. Plant growth monitoring with machine vision is a key component of automating plant care in CEA, to complement human visual assessments of plant health and provide input for adaptive closed-loop control of plant water and nutrients.
This project will build on mechatronic, electronic, robotic and software engineering skills to develop and evaluate machine vision-based algorithms for automated plant care in a controlled environment with artificial lighting. Relevant tasks will include placing machine vision cameras, performing laboratory trials of closed-loop plant care for example with a gantry robot, comparing day and night imaging, and developing and evaluating automated and robust software algorithms that enhance plant growth, health and yield.
This PhD scholarship is funded by the iLAuNCH Trailblazer, aligned with a current iLAuNCH project in which machine vision is being used to monitor plant growth in space. For more information on iLAuNCH, please visit ilaunch.space.

• Stipend of AUD $37,000
• Maximum period of tenure of an award is 3 years full-time.

To be eligible applicants must:
• have completed an Honours Degree with First Class or 2A Honours, or equivalent level, or a Master’s degree with a significant research component;
• have applied, or currently enrolled in the PhD program at UniSQ;
• not be receiving equivalent support providing a benefit greater than 75% of the student’s stipend rate;
• be an Australian citizen or permanent resident;
• be eligible to commence a PhD program at UniSQ Toowoomba in early 2025.

To be eligible applicants must:
• Have studied in robotics-related fields such as mechatronic engineering, electrical and electronics engineering, or computer science.
• Have experience in programming languages (e.g. Python) and machine vision and signal processing algorithms.
• Practical experience in electronic instrumentation and experimental trials is desirable.
• Ability to establish rapport with multidisciplinary industry representatives, for example related to Controlled Environment Agriculture, is desirable.

To apply, please ensure you have digital copies of the below information:

Curriculum vitae; encompassing any research presentations and/or publications
Education qualifications (testamur and academic transcripts) for all undergraduate and postgraduate awards,

Please directly contact Associate Professor Cheryl McCarthy by emailing cheryl.mccarthy@unisq.edu.au, who will advise you of the application steps.

5 April 2025

Further information about this scholarship can be obtained from Associate Professor Cheryl McCarthy by emailing cheryl.mccarthy@unisq.edu.au.