Determining crop phenology and/or crop type discrimination metrics using hyperspectral sensing technologies and machine learning algorithms for major winter crops in Australia

This Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

Supervisor – Dr Andries Potgietera.potgieter@uq.edu.au

The primary focus of this PhD position is to undertake functional research in the exploration and analysis of multi-temporal and -spatial hyperspectral sensing data. Developing and application of current and novel data fusion approaches to integrate climate, sensing and biophysical data for detecting of crop type and phenological stages for main winter crops across Australia. This PhD is part of the four year project funded and supported the GRDC funded  “CropPhen: Remote mapping of grain crop phenology and crop type prediction” project lead by UQ QAAFI (Project : UoQ2002-010RTX). 

Read more about the project and its application to industry here.

Preferred educational background

Applications will be judged on a competitive basis taking into account the applicant's previous academic record, publication record, honours and awards, and employment history.

A working knowledge of design and application of multi-modal data fusion algorithms to support systems modelling would be of benefit to someone working on this project.

The applicant will demonstrate academic achievement in the field(s) of remote sensing and/or machine learning (ML) or artificial intelligence and the potential for scholastic success.

A background or knowledge of ML software and libraries such as TensorFlow and/or Pytorch is highly desirable.

*The successful candidate must commence by Research Quarter 1, 2022. You should apply at least 3 months prior to the research quarter commencement date. International applicants may need to apply much earlier for visa reasons.

Apply now