The primary objective of the work/project:
At the present moment, the selection criteria for sorting mature somatic embryos are based on a manual process and they require an experienced operator.
Last financial year, we implemented the selection process machine learning (ML), and we have proven that ML is able to predict and correlate the morphological features of mature somatic embryos with germination success.
This year, we would like to develop the prediction model for mature somatic embryos originating from high-value industry embryogenic cell lines and apply deep learning approaches for picture annotation.
The 21st Century Tissue Culture Partnership is a project to fully automate the production of radiata pine varietals via somatic embryogenesis (SE), with the aim of reducing the overall cost of the current Petri dish-based protocols.
In the process of producing somatic embryos, the harvest of individual mature embryos from the culture is the most time-consuming/costly step. Within this project, this step is being automated with the development of a Fluidics system being designed to meet the specific selection criteria of radiata pine, based on image analysis to identify embryos most likely to germinate.
Last year, an external assessment of the Fluidics system highlighted the critical nature of the intellectual property being developed in the frame of ML and the importance of accelerating the piloting of the approach and its adaptation to current systems used in varietal production in New Zealand for early benefit creation.
This project aims to physically characterise mature somatic embryos of radiata pine originating from industry cell lines in order to establish selection criteria which will be required for the development of the image analysis part of the fluidics system and predict the germination success.
What the student will do
The student will get the opportunity to be involved in some steps of the in vitro propagation technology (somatic embryogenesis) used with Pinus radiata, maturation and germination. He/she will be expected to be involved hands-on in these stages and will be responsible for collecting data related to morphological features. They will be expected to use a microscope and image analysis software. This will give the student valuable experience in in-vitro propagation of pines, collection of data and microscopy in correlation with machine learning.
Qualifications, skills and attributes needed:
Minimum NCEA - level 3, with a strong science component
Excellent hand-eye coordination and an eye for detail
Some microscopy experience and IT skills would be an advantage
Date of the summer programme: 14 November 2022 - 3 February 2023
Applications close: 7 October 2022
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