Investment Details

PILOT

Welcome to the GRDC Investments pilot. It contains a selection of our RD&E investment portfolio. Let us know what you think!

GRDC Code: UOQ2002-008RTX
Machine learning applied to high-throughput feature extraction from imagery to map spatial variability

This project uses Machine Learning to develop high-throughput phenotyping (HTP) of crop canopy features. Plant images from the many project partners train machine learning models for this. The model training is done using the Weiner supercomputer at UQ. These models will be part of edge-computing units, which can take and process images offline.

These can be mounted on farm machinery and UAVs, or at the side of the paddock. This investment also works with the GRDC ‘CropPhen’ investment.

Project start date:
17/02/2020
Project end date:
16/02/2022
Crop type:
Wheat, Barley, Sorghum, Canola/Rapeseed
Region:
National
Organisation:
The University of Queensland