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Using machine learning to increase genetic gain in canola blackleg resistance breeding

This project will extend current research into deep learning and the genomics of disease resistance and apply findings to improve the blackleg resistance of Australian canola cultivars. Neural networks are a series of algorithms that mimic the operations of a human brain to recognise relationships between vast amounts of data. In this project, neural networks will be trained to associate canola blackleg resistance phenotypes with gene expression, whole genome sequence, and single-nucleotide polymorphism data from a range of different data sources. The neural networks will identify non-additive genetic effects of blackleg resistance that cannot be easily identified using standard multivariate statistical models.

Project date

17 Feb 2020-16 Feb 2022

Project funded by

Multiple industries
Alternative protein Cereal grains Other rural industries Pulse grains

Grains Research and Development Corporation (GRDC)

GRDC's purpose is to invest in RD&E to create enduring profitability for Australian growers. We invest in projects and …
  • Location

    Australia

  • Organisation type

    Research funding body

Focus areas

Technology areas

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