Plant biology meets computer vision

green computer

Interdisciplinary ANU research project reimagines plant phenotyping  

Thursday 2 December 2021
Last month, Canadian biopharmaceutical company Medicago R&D Inc. and ANU successfully completed the first phase of a five-year research collaboration to monitor the growth and performance of plants used in the production of Virus-Like Particles (VLPs).
 
The second phase of the collaboration will support $1.5M of research to enable Medicago to optimise proprietary plant-based technology that is used to produce vaccines and protein-based therapeutics.
 
Medicago uses a plant species that is indigenous to Australia – Nicotiana benthamiana – to produce VLPs. VLPs mimic the structure of viruses and can induce an immune response without causing an infection. When purified, VLPs could be used as vaccines to combat influenza, COVID-19 and other viral infections.  Medicago’s recombinant technology is rapid, versatile, and scalable. It is ideal for rapidly producing vaccines that match new strains of viruses, such as in the case of seasonal influenza and emerging strains of COVID-19.
 
Initiated and supported by the Centre for Entrepreneurial Agri-Technology (CEAT) and the Office of Business Engagement and Commercialisation (BEC) at ANU, the project is led by researchers from the Australian Plant Phenomics Facility (APPF), ANU Research School of Biology (RSB), ANU College of Engineering and Computer Science (CECS) and ANU Biological Data Science Institute.
 
In partnership with ANU scientists, Medicago seeks to gain further insight into the factors that regulate VLP production within individual plants. Central to that work has been the development of a way to non-invasively examine plants using advanced infrastructure and imaging technology. Subsequently, machine-learning approaches – like that used in facial recognition – are used to continuously map the canopies of plants.
 
The data, tools and models developed in the project will help Medicago rapidly and non-invasively assess factors influencing VLP production at scale.
 
The research team from CECS, consisting of Professor Hongdong Li and Dr Liang Zheng and research fellows and students, are developing advanced computer vision and machine learning techniques to support in vivo 3D plant modelling and non-invasive phenomics analysis for the Nicotiana benthamiana data collected by the APPF team.
 
“Existing 3D Computer Vision algorithms, while working well for modelling the 3D shapes of man-made objects such as a house or a car, often fall short in modelling complex natural biological shapes such as tree branches or plant stems and leaves. This cross-college interdisciplinary project provides a unique opportunity for computer scientists to be working closely with plant biologists in addressing the above scientific challenge. Together, they will jointly develop cutting-edge artificial intelligence technology and novel plant phenotyping methods, pushing the frontiers of both fields forward", said Professor Li. 
 
CEAT – led by Dr Owen Atkin and supported by Meredith Thomas and Mandy Nguyen – and BEC (Dr Lauren Du Fall) are proud to have supported the collaboration since its inception. ”It has been a pleasure to be involved in a project that harnesses the knowledge, expertise and infrastructure of the ANU to help increase vaccine production – particularly in the context of the COVID-19 pandemic” said Professor Atkin, Director of CEAT.

Updated:  10 August 2021/Responsible Officer:  Head of School/Page Contact:  CECS Marketing