Liqi Han

High-Throughput Phenotyping, Digital Twins and Artificial Intelligence for Strawberry Breeding and Planting

As agriculture undergoes rapid digital transformation, there is increasing demand for highly skilled, multidisciplinary researchers to unlock the full potential of emerging technologies in AgTech. In particular, candidates with expertise in AI, data science, remote sensing, and computational modelling are highly demanded by the industry.

We are seeking a motivated and talented PhD candidate to work on high-throughput phenotyping, digital twins and artificial intelligence for strawberry systems. The project primarily aims to accelerate strawberry breeding, while also exploring approaches to support planting systems as a secondary objective.

Project Overview

Strawberry production faces increasing demands for efficiency, quality, and resilience. This PhD project will focus on developing and applying high-throughput phenotyping methods (using technologies such as remote sensing and imaging systems) in combination of digital twins and artificial intelligence to rapidly assess plant traits as well as flowering and fruiting at scale. The outcomes will support better breeding cycles and improved decision-support in strawberry production systems.

We welcome applicants with backgrounds in:

• Computer Science

• Artificial Intelligence / Machine Learning

• Remote Sensing

• Data Science or related quantitative fields

• Agricultural Engineering or Precision Agriculture (with strong computational skills)

We expect the candidates to have:

• Strong analytical and programming skills

• Interest in interdisciplinary research combining agriculture and technology

The successful candidate will work closely with:

• Dr Liqi Han | Lead of DigiHort team | QAAFI Centre for Horticultural Science

• Dr Katie O’Connor | Senior Plant Breeder | Queensland Department of Primary Industries

The candidate will gain

• Experience at the intersection of AI, sensing technologies, digital twins, and agriculture

• Opportunity to contribute to real-world agricultural innovation

• Access to cutting-edge datasets, field trials, and research infrastructure

• Strong industry and research collaboration opportunities

Interested candidates are encouraged to contact Dr Liqi Han (liqi.han@uq.edu.au) and Dr Katie O’Connor (katie.oconnor@dpi.qld.gov.au) with a brief statement of research interest and CV.