Precision Weed Management
Using UAV-based weed detection, field mapping, precision application, and decision-support approaches to target weeds more accurately and reduce unnecessary inputs.
University of Manitoba
Department of Plant Science
Digital Agronomy & Weeds Lab
Advancing sustainable agriculture through precision technology and data-driven research
Field research meets digital agriculture
We combine weed science, agronomy, remote sensing, and data analysis to study practical crop and weed management questions.
Research themes
We combine weed science, agronomy, remote sensing, and data-driven methods to study crop and weed management in Prairie agriculture.
Integrated Weed Management
Combining herbicides with cultural, agronomic, and non-chemical tactics to improve long-term weed suppression and support resilient cropping systems.
Digital Crop Phenotyping
Applying LiDAR, multispectral imagery, RGB sensing, and field measurements to quantify crop growth, canopy structure, biomass, maturity, and yield-related traits.
Crop Management & Agronomy
Evaluating seeding date, seeding rate, row spacing, crop establishment, and crop-weed interactions to develop practical management recommendations for Prairie production systems.
Weed Adaptation & Resistance
Investigating herbicide response, stress memory, eco-evolutionary adaptation, and molecular mechanisms that help important Prairie weeds persist under agricultural selection pressures.
Principal Investigator
Dr. Dilshan Benaragama
Assistant Professor in the Department of Plant Science, leading research across weed science, agronomy, remote sensing, and precision agriculture.
Meet the teamLatest news
View allMediaSeptember 6, 2026
‘Big potential for farmers’: Manitoba researchers develop new tech to maximize farming, minimize chemical use
CTV News Winnipeg featured University of Manitoba researchers working on new farming technology designed to support more efficient and environmentally responsible crop management.
MediaAugust 25, 2026
Can farmers grow more while spraying less?
UM Today featured Dr. Dilshan Benaragama, student researchers, and collaborative precision-agriculture work aimed at turning drone and sensor data into practical, targeted field management.
Media2026
Using drones for precision weed control
In an AGronomyTV feature, Dr. Dilshan Benaragama discusses how drones and digital tools can support more precise weed management.
Recent publications
CWRepViT-Net: An encoder-decoder deep learning framework with RepViT blocks for crop weed semantic segmentation in soybean fields through their life journey
Masoomeh Gomroki, Dilshan Benaragama, Christopher James Henry, Nasem Badreldin, Robert Gulden
Smart Agricultural Technology
The impact of herbicide-resistant canola systems on the weed community dynamics in the Canadian Prairies
Theodore Chastko, Dilshan I. Benaragama, Julia L. Leeson, Christian J. Willenborg
Canadian Journal of Plant Science
Revisiting cropping systems research: An ecological framework towards long-term weed management
Dilshan I. Benaragama, Christian J. Willenborg, Steve J. Shirtliffe, Rob H. Gulden
Agricultural Systems