Pairing synthetic datasets with machine-learning models to improve harvest efficiencies
Process Optimization
Consortium Contribution
$219,926.85
Cluster Contribution
$179,940.15
MacDon Industries Ltd.
University of Manitoba
Goal
To accelerate the development and deployment of automation technologies for Canada’s agriculture sector
Project Summary
Throughout the project, MacDon and the University of Manitoba will create synthetic datasets that replicate field conditions, enabling faster model development, iteration and testing without relying on seasonal in-field data collection. This use of synthetic data will help the partners to reduce traditional model development timelines, giving them the opportunity to put it into Canadian farmers’ hands sooner.
This, combined with the technology’s potential to train machine learning (ML) models in farm machinery, will help improve on-farm productivity, reduce operator burden and improve operator effectiveness—leading to a more competitive Canadian food production and value-added agriculture sector.
