Using Machine Learning To Develop New Methods For Genetic Gain In Crops Challenged By Fungal Diseases
This project will apply a machine learning approach to genetic and high throughput phenotyping data to draw out genetic markers associated with resistance to fungal infections in wheat, canola, lentil, and chickpea crops.
Project date
17 Feb 2020-16 Feb 2022
Research organisations
Curtin University
Project funded by
Multiple industries
Alternative protein
Cereal grains
Other rural industries
Pulse grains
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