Developing prairie-centred machine learning algorithms
EMILI is working with the TerraByte research team which is led by researchers at the University of Winnipeg and University of Manitoba, to develop a publicly accessible database of labelled images of plants and weeds which will be used to train machine learning models for Prairie-centric plant phenotyping, disease assessment, and weed management. This will allow us to develop and test crop scanning technology capable of identifying weeds amongst Western Canadian crops. These data sets are central to developing digital agriculture solutions that work in Manitoba. Breeders can use the phenotyping data to help select for more tolerant, higher yielding crops. Farmers will use weed identification Al to identify, measure, locate and assess the severity of weeds or diseases in their fields, allowing them to make site specific data driven decisions.
