Bhatti, Moid RiazAkyol, AliRosigkeit, HenrikMatzke, LindaGrabenhorst, IsabelGómez, Jorge MarxWohlgemuth, VolkerNaumann, StefanArndt, Hans-KnudBehrens, GritHöb, Maximilian2022-09-192022-09-192022978-3-88579-722-7https://dl.gi.de/handle/20.500.12116/39395The research project "5G Smart Country" aims at developing ideas for the development and testing of 5G applications for agriculture and forestry under real conditions. Agricultural and forestry data are collected from a wide variety of sources, such as satellites, drones, and robots with special sensors. Artificial intelligence (AI) and data analytics algorithms help make the required decisions, particularly for automatic differentiation between crops and weeds for mechanical weed control, demand-driven fertilization (variable rate application, VRA)—also by means of small-scale application (pointed fertilizing)—automated tracking of wildlife populations, real-time assessment of harvest (smart harvesting), forest inventory maintenance, and targeted logging. Here we present a system architecture and software model for digital crop management and show how multispectral analysis is used to develop vegetation indices to conduct VRA.enLiving labsmart farmingBMDV5Gsite-specific fertilizationVRAsatellite/sentinelvegetation indicesNDVIGNDVIdigital plant modelAI.Text/Conference Paper1617-5468