ML Project D: Extracting value from crop/soil variability mapping

This tender consists of seven interrelated procurements. The applicant may apply for all seven Projects A-G, but successful applicants will only be contracted for Project A alone, any combination of Projects B-G, or all Projects B-G.

For full details, including links to relevant documents and information on any Questions and Answers, visit:

https://grdc.com.au/research/applying-and-reporting/current-procurement/open-tenders

Background:

There is an opportunity to provide growers with enhanced, machine learning (ML)-driven, 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.

Evaluation Criteria, Outcomes and Outputs:

Applications must address the evaluation criteria, outcomes, and outputs relevant to the specific procurement being applied for.

For details visit:

https://grdc.com.au/research/applying-and-reporting/current-procurement/open-tenders