Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Current agricultural practices include blanket application of herbicides for eliminating unwanted plant speciesx000D
before they can steal resources and cause harm to agricultural cash crops cultivated by farmers. Applyingx000D
herbicides to entire fields instead of individual plants is wasteful. However, identifying and targeting individualx000D
weeds has not been feasible in large-scale food production. While much progress has been made, thesex000D
techniques are still not robust enough to accurately differentiate between weeds and crops, especially inx000D
real-world conditions consisting of multiple weed species within a single field. Thus, the aim of this Engagex000D
Grant is to create a real-time system to classify digital images of weeds and crops, where the goal is reducingx000D
the amount of herbicide used in food production, ultimately leading to increased crop yields and lower costs.x000D
The focus of the proposed system is to classify the crop species of corn and canola as well as the weeds Canadax000D
thistle, cocklebur, redroot pigweed, and dandelion using deep neural networks. The benefits includex000D
environmental footprint reduction for Canadian farmers, and lower food production costs.