Image-based phenotyping to accelerate analysis of breeding field trials

Plant breeding companies develop new seed varieties by growing thousands of candidate varieties in large field trials and manually selecting the best-performing varieties. Variety selection is informed by genomic data, but plant populations still need to be grown in field conditions and screened for specific physical characteristics, called phenotypes, such as yield, disease resistance, harvestability and other important traits. In-field measurements of these phenotypes are expensive, tedious and time consuming, and much of the collected data is not directly used by breeders. Digital phenotyping has the potential to efficiently provide critical information to plant breeders by analyzing aerial images of field experiments collected by unmanned aerial vehicles (UAVs). However, the process of transforming UAV images into actionable information for plant breeding companies remains a significant bottleneck in this important process. To capitalize on the potential efficiency and genetic gain enabled by UAV based imaging of field trials, we propose to work with Nuseed Canada to integrate and refine our PlotVision software into Nuseed Canada processes for their crop breeding field trials.

Faculty Supervisor:

Ian Stavness

Student:

Partner:

Nuseed Canada

Discipline:

Computer science

Sector:

Agriculture

University:

University of Saskatchewan

Program: