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Grant: Alliance Grants

University of Manitoba

$262,899.00

Remote sensing weed-crop interference and data-driven weed management decision making tools

EF 2024-25

Résumé
As herbicide-resistance (HR) continues to increase, a new approach and new tools for weed management are imperative. In partnership with the main crop commodity groups in Manitoba (Manitoba Crop Alliance, Manitoba Canola Growers Association, and Manitoba Pulse & Soybean Growers) and the Western Grains Research Foundation, we are conducting a multi-commodity research project designed to develop tools that reduce the frequency and quantity of herbicide applications in agriculture. This partnership was forged to help mitigate the risk of HR weeds, reduce the pesticide load on the environment, and fill a critical gap in capacity to train the next agronomic leaders in advanced technologies for sustainable systems-based integrated crop protection. All, while maintaining crop productivity to help ensure Canada's food supply chain remains safe, strong, and stable, to meet rising global demand for Canadian agricultural products. This project will train 22 HQP (10 undergraduate, 6 MSc, 4 PhD, 2 PDF). This research project will deliver precise herbicide application decision-making tools based on remote sensing technologies, in-season monitoring, and rapid screening tools for identification of weed species and HR weed biotypes. Remote sensing and AI algorithms will be used to determine crop-weed interference throughout the growing season and this information will be fed into a data driven algorithm that determines the need for weed control to be fed into GPS guided equipment that manages weeds in a site-specific manner. The technology will also be able to detect HR weed patches as they develop, providing an early-detection, rapid-response approach that is a critical component for managing new HR weed biotypes. The project provides a unique opportunity for researchers and industry leaders to work together to solve a paramount production issue in agriculture. The effective weed management tools created are expected to help reduce herbicide applications and the selection pressure for HR weeds, while meeting Canada's global food production, environmental, and food security goals.
Programme
Alliance Grants
Emplacement
Manitoba, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
581253-2022
Name nom
Gulden, Robert RH
Department département
Plant Science
Organization id
19
Institution établissement
University of Manitoba
Province en
Manitoba
Province fr
Manitoba
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2022
Award amount
262899
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
506
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Agricultural chemicals (fertilizers, herbicides, pesticides)
Area of application fr
Produits chimiques agricoles (engrais, pesticides, herbicides)
Research subject code
4900
Research subject group en
Plant and tree biology
Research subject group fr
Biologie végétale et des arbres
Research subject en
Plant and tree biology
Research subject fr
Biologie végétale et des arbres
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Remote sensing weed-crop interference and data-driven weed management decision making tools
Keywords
artificial intelligence; crop-weed interference; data-based decision-support systems; remote sensing; yield loss modelling
Application summary
As herbicide-resistance (HR) continues to increase, a new approach and new tools for weed management are imperative. In partnership with the main crop commodity groups in Manitoba (Manitoba Crop Alliance, Manitoba Canola Growers Association, and Manitoba Pulse & Soybean Growers) and the Western Grains Research Foundation, we are conducting a multi-commodity research project designed to develop tools that reduce the frequency and quantity of herbicide applications in agriculture. This partnership was forged to help mitigate the risk of HR weeds, reduce the pesticide load on the environment, and fill a critical gap in capacity to train the next agronomic leaders in advanced technologies for sustainable systems-based integrated crop protection. All, while maintaining crop productivity to help ensure Canada's food supply chain remains safe, strong, and stable, to meet rising global demand for Canadian agricultural products. This project will train 22 HQP (10 undergraduate, 6 MSc, 4 PhD, 2 PDF). This research project will deliver precise herbicide application decision-making tools based on remote sensing technologies, in-season monitoring, and rapid screening tools for identification of weed species and HR weed biotypes. Remote sensing and AI algorithms will be used to determine crop-weed interference throughout the growing season and this information will be fed into a data driven algorithm that determines the need for weed control to be fed into GPS guided equipment that manages weeds in a site-specific manner. The technology will also be able to detect HR weed patches as they develop, providing an early-detection, rapid-response approach that is a critical component for managing new HR weed biotypes. The project provides a unique opportunity for researchers and industry leaders to work together to solve a paramount production issue in agriculture. The effective weed management tools created are expected to help reduce herbicide applications and the selection pressure for HR weeds, while meeting Canada's global food production, environmental, and food security goals.