Breeder Facing AI-Tools project data sets

The Agriculture and Food (A&F) Research Unit is leading a Grains Research and Development Corporation (GRDC) investment funder through GRDC's Accelerating Genetic Gain Initiative, in partnership with the University of Queensland, Agrisapiens, the University of Western Australia and the Australian National University. The project will develop breeder facing AI tools to maximise use of pre-breeding outputs and improve breeding processes and will be delivered over a 3-year term (completion November 2028). 

A wealth of pre-breeding genetic resources, populations of diverse, unimproved or non-elite material that breeders tap into to improve crops, and associated data have been generated in Australia’s primary grain crops this century. The translation of these resources into value for breeding programs has not been realised to its full potential, due to a lack of accessible, validated tools and methods to support efficient introduction of pre-breeding diversity into breeding programs. In this project, a multi-disciplinary team brings together the necessary experience and capability to address this challenge. Recent advancements in artificial intelligence (AI) offer potential to better integrate diverse data sources, including genetic, phenotypic and environmental data, and equip breeders with the capability to optimise how pre-breeding diversity is leveraged in breeding programs to generate superior genotypes more efficiently. The project team will develop a data driven solution that address breeder-identified gaps in an AI supported ‘Pre-Breeding Optimisation Engine’. The project aims to benefit Australian crop breeding companies through dissemination of project outputs to the Australian grain crops industry. This will translate into growers having access to diverse wheat, canola and barley varieties with greater yield gains and resilience, supporting Australia’s capacity to remain internationally competitive in global grain markets.

The purpose of this private DAP collection is to support data sharing among partners within the project, subject to relevant MTA and data sharing agreements. This includes genome SNP and sequence data, phenotype data, environmental and management meta-data for wheat, barley and canola.

A separate DAP repository will be made available at completion of the project to enable sharing/distribution of relevant project outputs for research or commercial use.

Anyone interested in the project, associated data assets or outputs should contact Shannon Dillon via email, [e-mail]