Precision agriculture links research infrastructure, farm trials and processing. Distinguish spray-drone potential from unresolved drift, dose and regulatory constraints.
WUR established UARSF in 2012. Work has progressed from calibration and processing to farm roadmaps and weed-search planning, with auditability prominent in 2026 policy debate.
30 sources
Teaches choosing multispectral, thermal and LiDAR sensing, evaluating outputs and linking them to crop decisions.
Connects measurement, analysis, decisions and implementation with farm trials, as-applied records and other sensors.
An end-to-end mission-design course begins with the agricultural question and ends with useful visualisation.
Established in 2012, the facility bridges satellite and ground sensing through dedicated experiments, calibration and workflows.
The regulator describes drone inspections with controlled launch areas, identifiable crews and privacy safeguards.
Studies orchard mapping and fruit perception under repeated rows, occlusion and changing light.
A reinforcement-learning study trades shorter search paths against missed weeds using prior maps and in-flight detections.
A 2025 farmer–operator–research report explores spray drones; aviation risk procedures do not themselves authorise pesticide use.
A practice-based roadmap separates established monitoring from application challenges and calls for more field experience.
A 2026 policy assessment emphasises drift and dose evidence plus inspectable location, time and concentration records.
A dataset paper connects RGB, multispectral, yield and weather data from five Japanese and Dutch trials with MIAPPE metadata.
Adapts routes to crop rows and vegetation; discusses LiDAR accuracy versus processing time and validation in a Spanish vineyard.
Compares soil nematode density with UAV indices and distinguishes detection ability from reliable yield-damage estimation.
Research combines observation with spraying and goods delivery, alongside platform development and capacity building.
Compares 15 m and 30 m imaging for crop height, volume, cover and vegetation indices.
The public thesis abstract compares hyperspectral UAV and ground sensing and examines limitations of disease applications.
A programme identifies regulatory complexity, weak business cases, data ownership, hardware dependency and fragmented knowledge as barriers.
A 2026 Lelystad potato campaign follows growth stages and aligns ground, airborne and satellite observations.
A consortium links autonomous equipment and drone data with FAIR management, yield prediction and potato data passports.
A 2019 review maps UAV sensing and processing to orchard geometry, physiology and health.
A proposed MSc project combines target detection, replanning and nozzle control; it is a research brief, not a proven product.
Bibliographic details confirm a 28-page 2026 report; PDF access was denied and full-text review remains incomplete.
Positions vision, AI, robots and drones within integrated weed management rather than treating spraying as the only response.
Connects complex models with practical apps and decision support validated in greenhouses, orchards and fields.
A testing service combines controlled plots, yields, weed species and drone time series to validate AI and robots.
The 2022–2025 plan includes drones in the aerial-spraying prohibition; use it as policy history, not a substitute for current checks.
Official guidance links risk categories to aircraft, location and purpose, relevant to heavier agricultural drones.
Candidate report on application standards and evaluation; only search excerpts were available and PDF access returned 403.
A 2015 Omni-Drones feasibility project links repeated observation with fertiliser and weed decisions; page access timed out.
Candidate feasibility project using thermal drones to assess orchard frost protection; full page was unavailable.
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