Designing Crops for Global Food Security

Plant Phenotyping and Imaging
Research Centre (P2IRC)

P2IRC is a digital agriculture research centre funded by the Canada First Research Excellence Fund (CFREF), managed by the Global Institute for Food Security* (GIFS), and located at the University of Saskatchewan (USask).

(*GIFS Measures in Response to the COVID-19 Situation)

 

 

A food-secure tomorrow. Harnessing machine learning to create climate-smart crops. Click image to learn more.

Accelerating crop development by linking specific genes to desired traits

P2IRC was founded in 2015 with $37.2 million awarded to USask by the Canada First Research Excellence Fund (CFREF). The CFREF helps Canadian universities gain global competitive advantage and implement large-scale, transformational, and forward-thinking institutional strategies. 

Why P2IRC? 

A growing population and different resource challenges have made food security a major issue facing the world today. Sustainable agricultural technology is key to feeding the world more resourcefully and will help breed more climate resilient crops faster, with reduced environmental impact.

A digital agricultural research centre, P2IRC is developing innovative tools to revolutionize crop improvement by accelerating the process of plant breeding and transforming food production capacity. The P2IRC program is generating a range of data-rich technologies, products, and services that can fundamentally transform seed and plant breeding of large-area crops essential to global food security, including wheat, canola, and lentils.

As a leading agricultural hub tackling global food security challenges, by 2022, P2IRC will be the unique resource for plant breeders around the world.

Research Programs

Yield stability 

Combining the power of genomic and physiological selection for yield stability in a changing climate. 

Mobilizing Root-Soil-Microbiome Interactions

Developing methods to investigate the role of root traits in controlling yield, yield stability and quality traits.

Deep Learning for Phenomics

Developing deep learning methods for the automated estimation of phenotypes, to better capture genotype x environment interactions.

Field Imaging for Phenotyping 

Developing automated workflows to phenotype field-grown crops throughout the growing season using multiple imaging methods.

This research is underway thanks in part to funding from the
Canada First Research Excellence Fund.