AI-driven decision support tools for alfalfa’s winter survival and persistency

Alfalfa forage is the queen of forage in Canada, both for its nutritional value and for its global distribution across Canada; however, its weakest feature is poor winter survival and persistence. This parameter is affected by a multitude of environmental and management factors making it difficult to understand the reasons for this low persistence. This project will develop a Diagnostics and decision support tool using machine learning integrating proximal and remote data collected in the field to allow producers and field advisors to scout using drones, to understand the reason for the problem and to identify the best management practice to adopt for improving winter survival and persistency. The tool developed during the project will be accessible under cloud services to all producers and field advisors in Canada and will result in increased productivity, profitability and a reduction in the environmental footprint.

Faculty Supervisor:

Viacheslav Adamchuk;Karem Chokmani;Saeid Homayouni

Student:

Partner:

Canadian Forage and Grassland Association

Discipline:

Life Sciences

Sector:

Agriculture

University:

McGill University; Université du Québec : Institut national de la recherche scientifique

Program: