Work

Improved herbicide stewardship with remote sensing and machine learning decision-making tools

Year: 2026

Type: article

Abstract: Improved herbicide stewardship with remote sensing and machine learning decision-making tools Weeds pose the most persistent and costly threat to crop production in Canada, driving widespread herbicide use and accelerating the rise of herbicide-resistant species. This article explores how emerging AI- and trait-based decision tools can transform weed management and usher in a new era of precise, sustainable herbicide stewardship. Weeds are the most consistent, recurring and damaging biotic threat to crop production in Canada. Without the use of herbicides, yield losses due to weeds can easily exceed 50% (1,2). Herbicides are applied on almost all Canadian fields and account for over 75% of total pesticides used in Canada. Intensive prophylactic use of herbicides has selected for herbicide-resistant (HR) weed biotypes, which now infest about 24 million acres of the Canadian Prairies and cost producers over C$600 million annually (3). Herbicide resistance continues to rise. The issue is not unique to Canada.

Source: Open Access Government

DOI: 10.56367/oag-049-12386

Authors: Robert H Gulden, Dilshan Benaragama, Nasem Badreldin

Institution: University of Manitoba

Language: English

Topic: Weed Control and Herbicide Applications

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Life on land

Open Access status: hybrid

OpenAlex ID: W7125150858