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”.
NC State’s AIRS project is developing repeatable drone data collection and analysis across agricultural research stations, with software already released and remotely initiated flights forming a further stage of development.
University of Missouri researchers developed FieldVision to allocate drone image-processing tasks across onboard, edge and cloud computers, reporting improvements in simulation as networking and computing conditions changed during missions.
The University of Bonn announced Stefano Mintchev’s appointment to advance interactive drones that use flexible structures, tactile sensing and control methods suited to close work in natural agricultural environments.
A four-year collaboration led by the University of Hawaiʻi will combine environmental sampling and AI to investigate earlier warning of biological and chemical threats in food production systems.
Charles Sturt University has developed SkyEye, a drone-based monitoring system that analyses farm imagery and provides alerts intended to help growers identify crop stress, pests and irrigation problems.
University of Florida, USDA and NASA researchers used hyperspectral imaging to identify early drought stress in lettuce, supporting development of non-destructive monitoring methods for greenhouses and other tightly controlled growing environments.
A University of Idaho-led research collaboration is developing harvest-integrated measurements and AI models to connect crop genetics with stalk strength, aiming to improve breeding for resistance to bending and breakage.
UC Merced researchers are developing materials that absorb ultraviolet light and emit visible wavelengths, exploring whether molecular design can improve greenhouse light management under the Central Valley’s demanding growing conditions.
UC Merced has introduced mobile greenhouse and agricultural-technology trailers to bring practical demonstrations of plant production, sensing, drones and automation to schools, communities and agricultural workplaces across the Central Valley.
Texas Tech and Texas A&M AgriLife Research are developing a multi-scale framework combining crop measurements, machine learning and growth models to improve irrigation decisions in regions with constrained water supplies.
Arkansas field trials tested a camera-guided push-cart sprayer for green weeds in dormant bermudagrass, reporting lower herbicide volume than broadcast treatment while exposing the importance of detection conditions and field variability.
Arkansas researchers report that different sensing platforms can reveal similar crop patterns, while separate rice trials show why spray-drone flight speed must be assessed alongside deposition and actual disease control.
