Following the machines, people and ideas shaping agriculture. Selected developments, clear context and a link to the original source.
Robotics. Intelligence. Agriculture.
137 stories matching “Data”.
Michigan State University reports wider use of its irrigation app alongside research comparing soil sensors, evapotranspiration models and sap-flow measurements for scheduling water applications in apple orchards.
University of Colorado Boulder researchers tested inexpensive printed sensors that tracked soil acidity across varied soils and operated outdoors for months, advancing continuous monitoring beyond occasional soil sampling.
AMAZONE announced an AI-assisted identification function for its mySpreader app, using a photograph and reference template to suggest a fertiliser match and initial machine settings for the operator to check.
Aigen launched its Alchemy world model to generate and label agricultural training scenes, claiming faster adaptation of its weeding robots to new crops while leaving independent field benchmarking outstanding.
The University of the Western Cape is developing a demonstration that combines solar generation, crop production, water treatment and cooling, with the aim of testing connected agricultural infrastructure under local conditions.
An Irish research collaboration will combine milking-machine data, sensors and machine learning to examine how equipment settings can better match individual cows’ physiology while supporting milk quality and comfort.
Source.ag’s Cloud Modules connect normalized greenhouse data with business intelligence tools and event-driven applications, aiming to simplify information exchange across facilities with different measurement and reporting conventions.
Blue Radix has launched strawberry support for Crop Controller, connecting greenhouse measurements and weather forecasts to existing climate computers for automated climate decisions throughout the growing cycle.
Priva and IUNU have integrated climate records and plant observations to produce rolling yield forecasts, giving greenhouse teams an earlier view of changing harvest volumes and operational requirements.
USDA’s data-modernisation plan includes a pilot combining improved satellite imagery, crop models and producer information, alongside evaluation of AI and machine learning within upgraded agricultural data systems.
Participants at an Arkansas agriculture symposium argued that scientific reasoning and critical evaluation remain essential as AI becomes embedded in farm equipment, data analysis and the tools students will use professionally.
Montana and Washington State researchers tested RGB drone imagery for Potato Virus Y assessment, reporting promising field-level surveillance potential but insufficient performance for dependable diagnosis of individual plants.
