Developing machine learning algorithms
EMILI is working with the National Research Council of Canada and the University of Winnipeg to develop image collections of pea plants and their root systems. High-throughput imaging and agronomic measurements on 12 varieties of field peas will complement machine learning algorithms to better differentiate pea plants from weeds while testing the associations between plant biomass, protein, Rhizobium nodules, and yield.
Rapid phenotyping analyses from machine learning will allow pea breeders to select for increased resilience and higher yields faster than previously possible. The move towards high-throughput digital phenotyping through computer vision will help modernize crop development efforts for more efficient and effective selection of traits to benefit Canadian agriculture.
