Following the June 2026 federal policy change, provincial rules and label conditions still need separate confirmation.
After research and residue assessment, the RPAS policy for products registered for conventional aerial application changed in 2026.
30 sources
Covers labels, provincial rules, pilot/loading role separation and contamination prevention including battery changes.
Announces the June 30, 2026 policy change while retaining federal/provincial requirements and certification.
Defines conditions and assessment evidence; conventional rotary-aircraft buffer zones remain while RPAS drift models develop.
Ontario's proposed regulatory pathway; this consultation is not itself an operational permission.
Summarizes pesticide-policy changes and safety assurance; an acceptance letter is distinguished from a compliance determination.
Consultation preceding the 2026 policy; current operations should use the final SPN2026-02 document.
Announces provincial RPAS licensing, drift liability coverage and GPS records; product-registration counts are a January 2026 snapshot.
A weed-management program prepares drone spot-spray trials for 2027; planned tests are separated from existing results.
A trial spread forage seed when rain prevented ground equipment access; long-term establishment and yield require further assessment.
Trials winter-wheat broadcasting into standing soybeans; timely seeding still faces surface drying and establishment failures.
Combines aerial, ground and satellite sensing for breeding, weeds and nutrient research.
Describes trials comparing fungicide efficacy and carrier-water volumes in peas, wheat and canola; it does not establish final outcomes.
Supports a lettuce decision system combining imagery and weather forecasts, illustrating analytics tied to grower decisions.
A 2026/27 project proposes autonomous greenhouse navigation and pollen delivery; it is a research plan, not proven yield improvement.
An ongoing Alberta demonstration evaluates forage establishment after drone interseeding using imagery, soil sensors and field scouting.
A research project links multispectral pest-risk scouting in corn with drone delivery of predatory mites in soybeans; reported benefits remain prospective.
The 2026–27 project combines calibrated multispectral imagery, weather stations and crop physiology measurements to develop and validate drought detection.
A farmer demonstration reports using drone stress maps to identify uneven irrigation and inform sprinkler changes; this is a case report rather than a controlled yield trial.
Wild blueberry growers and university researchers co-develop weed maps, farmer-facing recognition tools and spray trials where the specialist market attracts limited large-manufacturer R&D.
A Canada–Italy experiment used repeated RGB and multispectral imagery to model lentil growth and link canopy traits with genetic markers across environments.
A short project description proposes optical drone sensing of soil organic carbon; the page provides no validation accuracy or deployment results.
A crop researcher describes scouting and stress assessment alongside weather limits, analysis expertise and flight-planning requirements.
Canola seedling counting translates imagery into plant population and ground-cover estimates that can support reseeding decisions.
An industry–university project develops multispectral applications for potato and greenhouse disease detection and crop-insurance damage assessment.
A shared phenotyping program connects repeated imaging, automated processing, interactive plot inspection and crop-management research.
Since 2021, imagery and ground scouting have supported hail-damage assessment research; RGB imagery did not visually distinguish damage severity in the reported 2023 work.
Multi-site trials evaluate herbicide injury and desiccation. Held-out sites and years reduced prediction performance, demonstrating limits to model transferability.
A project proposes simultaneous drone mapping of field rocks with ground verification to inform removal and reduce machinery-damage risk; results are not reported here.
The thesis abstract reports time-dependent kochia discrimination in wheat and lentil; strong late-season classification does not establish early-season control performance.
A UBC Farm testbed combines real crops, cellular connectivity, sensors and farm software for validating connected robotics and aerial monitoring.
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