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Using Machine Learning To Develop New Methods For Genetic Gain In Crops Challenged By Fungal Diseases

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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.
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Development and validation of soil amelioration and agronomic practices to realise the genetic potential of grain crops grown under a high yield potential, irrigated environment in the northern and southern regions. logo
Research project

Development and validation of soil amelioration and agronomic practices to realise the genetic potential of grain crops grown under a high yield potential, irrigated environment in the northern and southern regions.