The Australian Grains Research and Development Corporation (GRDC) has published seven separate request for tenders (RFT) this week, seeking help with machine learning projects that aim to aid the Australian grains industry.
The GRDC said it has identified machine learning as a foundational technology with the potential to deliver value to Australian grain growers by assisting the GRDC "address a broad range of production constraints and opportunities". The opportunities are determined under its Research, Development and Extension Plan 2018-23 [PDF].
The GRDC is a statutory authority established to plan and invest in R&D for the Australian grains industry
According to the GRDC, the tenders are seeking the assistance of the "machine learning community" to develop solutions that are relevant to specific issues grain growers and researchers in the field are faced with.
Specifically, the GRDC wants to stand up partnerships between providers and its researchers to generate a "pipeline of machine learning-driven products that address grains industry constraints and opportunities".
It said it would establish a machine learning Technical Consultation Group (TCG) to advise the GRDC.
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There are a handful of use cases determined by the GRDC, but it is also seeking applicants to put forward some other ideas. They are to be short-term projects, the tender documents said, noting that they would be used to shape future, longer-term initiatives.
One of the projects the GRDC has highlighted is determining if machine learning can help the grains industry improve its understanding and identify genetic material that can contribute to robust crop stress tolerance.
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Another project is centred on improving grower decision-making by helping provide more accurate weather and/or climate forecasts; while the GRDC is also hoping machine learning can help gain insights from crop and soil constraint data.
"A wealth of field-mapped data layers is becoming available: Yield, multispectral, topographic, electromagnetic, electroconductive, and weather. Whilst commercial mapping analytics are available, ML models could be developed that are better-able to account for spatial variability," it wrote.
It also wants to be able to access information stored in GRDC reports and other research publications.
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