Reference number : GC-2017-Q2-00352, amendment number : , agreement type : G, recipient legal name : Henry, Christopher (The University of Winnipeg), recipient country : CA, recipient province : nan, recipient city : Manitoba, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Current agricultural practices include blanket application of herbicides for eliminating unwanted plant species_x000D_
before they can steal resources and cause harm to agricultural cash crops cultivated by farmers. Applying_x000D_
herbicides to entire fields instead of individual plants is wasteful. However, identifying and targeting individual_x000D_
weeds has not been feasible in large-scale food production. While much progress has been made, these_x000D_
techniques are still not robust enough to accurately differentiate between weeds and crops, especially in_x000D_
real-world conditions consisting of multiple weed species within a single field. Thus, the aim of this Engage_x000D_
Grant is to create a real-time system to classify digital images of weeds and crops, where the goal is reducing_x000D_
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 Canada_x000D_
thistle, cocklebur, redroot pigweed, and dandelion using deep neural networks. The benefits include_x000D_
environmental footprint reduction for Canadian farmers, and lower food production costs., program purpose french : Current agricultural practices include blanket application of herbicides for eliminating unwanted plant species_x000D_
before they can steal resources and cause harm to agricultural cash crops cultivated by farmers. Applying_x000D_
herbicides to entire fields instead of individual plants is wasteful. However, identifying and targeting individual_x000D_
weeds has not been feasible in large-scale food production. While much progress has been made, these_x000D_
techniques are still not robust enough to accurately differentiate between weeds and crops, especially in_x000D_
real-world conditions consisting of multiple weed species within a single field. Thus, the aim of this Engage_x000D_
Grant is to create a real-time system to classify digital images of weeds and crops, where the goal is reducing_x000D_
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 Canada_x000D_
thistle, cocklebur, redroot pigweed, and dandelion using deep neural networks. The benefits include_x000D_
environmental footprint reduction for Canadian farmers, and lower food production costs., agreement title english : Real-time, machine-learning weed detection system for autonomous agricultural machines, agreement title french : Real-time, machine-learning weed detection system for autonomous agricultural machines, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-08-23, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada