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Fig. 1. Segmentation of high-resolution RGB images in soybean with the original image (top) and the segmented mask overlaid on the original image (bottom). Soybean plants (green) were seeded at five different densities, and the natural weed community has been segmented into grasses (pink), broadleaf weeds (dark orange) and volunteer canola (orange). Volunteer canola is glyphosate-resistant and would require alternate management compared with all other weeds in the image.

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.

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