25 stories matching “Training”.

The 2025 GreenTech Americas programme in Querétaro connected hydroponic cultivation, labour efficiency, irrigation and climate adaptation, bringing protected-crop technology discussions into a Latin American production context.
Source GreenTech Americas

The University of the Free State’s new greenhouse combines applied crop trials with training in data-led management under semi-arid conditions.
Source University of the Free State

Measured plant models and synthetic vision data are helping researchers compare robot designs in a greenhouse environment that can be reset.
Source Wageningen University & Research

University of León researchers publish annotated field images collected by a ground robot, providing a reusable resource for crop-detection development.
Source Data in Brief / University of León

KNUST and international partners plan low-cost electrical resistivity monitoring to study how farming practices affect water beneath the soil surface.
Source KNUST Department of Physics

Arkansas graduates won two competitions in Indianapolis, including a strawberry processing prototype combining vision, motion control and safety systems.
Source University of Arkansas

The June 2027 organizer programme connects research visits, machinery companies and field demonstrations across Osnabrück, Potsdam and Odense.
Source Adaptive AgroTech organizer announcement, shared by Eduardo Navas Merlo

FAO’s Central and Western Region locust commissions launched a programme combining classroom, simulator and field instruction for national surveillance and response teams.
Source FAO

A Ghana–Belgium partnership is developing locally grounded maize-yield estimation while addressing the field-data mismatches that can undermine satellite models.
Source GEOGLAM

A five-day Côte d’Ivoire workshop linked flight operations, image analysis and shared breeding services for participants from seven West and Central African countries.
Source AfricaRice

KALRO and CGIAR partners are combining historical trials and machine learning to develop maize nutrient recommendations adapted to local conditions.
Source Alliance of Bioversity International and CIAT

IRRI and Vietnamese partners report cooperative-led adoption of equipment that places seed and fertilizer at different depths to reduce input waste.
Source CGIAR Scaling for Impact