Following the machines, people and ideas shaping agriculture. Selected developments, clear context and a link to the original source.
Robotics. Intelligence. Agriculture.
305 stories matching “Research”.
Adaptive AgroTech’s updated organizer programme outlines a June 2027 tour through German and Danish research, industry and field sites, connecting agricultural robotics and digital farming with practical demonstrations and technical exchange.
University researchers report that a LiDAR-based retrofit adapts individual sprayer nozzles to vegetation density and has been used by three vineyard growers.
A Wageningen PhD project investigates robots that move their cameras and learn which viewpoint will provide better information about obscured plants.
A site-specific cultivator study combines RTK-guided field operation with adaptive-control research and reports 96.5% efficacy measured by tillage length.
Researchers are using repeated LiDAR and panoramic mapping to build a vineyard digital twin that reflects seasonal changes affecting autonomous navigation.
A German university has received and begun adapting a remotely controlled tracked machine for a planned stone-collecting agricultural robot.
A 2026 research paper investigates a mobile data-collection robot designed to observe individual strawberry plants and support greenhouse harvesting decisions.
A conference paper outlines a vision-guided robotic pollination platform for strawberries grown in multi-tier urban-farm environments.
Researchers from NTNU and Kilter developed and simulated a multi-robot approach to precision weed treatment. Their results suggest that distributing fieldwork among several AX-1 robots could reduce completion times.
A new study reports that plant architecture—including stalk length and fruit position—can markedly affect cucumber-harvesting robot performance.
A three-year study of fruit-growing operations finds that AI-enabled machinery changes agricultural work rather than simply removing the need for skilled people.
Brazilian researchers compared several soil-sensing methods across three farms, finding that carefully chosen combinations could retain useful predictive performance while avoiding the complexity of adding redundant measurements.
