Auflistung nach Autor:in "Grabenhorst, Isabel"
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- KonferenzbeitragDevelopment of a smart farming dashboard based of 5G mobile Data(EnviroInfo 2023, 2023) Akyol, Ali; Chahin, Rami; Dillschneider, Eva-Marie; Gerloff, Lars; Grabenhorst, Isabel; Gómez, Jorge Marx; Patil, Akhil; Schattenberg, Jan; Sgraja, Marie; Walther, Sören; Weide, JulianThis work in progress paper is written as a short description mainly of the backend of project 5G, which is in the field of smart farming. The project focuses on using different technologies and machines for weed management. This work in progress paper highlights the need for efficient weed management. It discusses the problems which are associated with weed management and it raises questions that need to be addressed in this domain. Moreover, the topic of using weed management 5G, UAV (unmanned aerial vehicle) and field robotics in agricultural and farming services is an important topic at present. Besides, the work in progress paper shows possible technical concepts and processes which can be implemented into smart farming to increase its efficiency. This paper discusses special methods, which can be used in weed management by using AI (artificial intelligence). In addition to the project description, the paper includes an evaluation of the current state of the research and an outlook of potential future research.
- KonferenzbeitragLiving lab research project "5G Smart Country" - Use of 5G technology in precision agriculture exemplified by site-specific fertilization(EnviroInfo 2022, 2022) Bhatti, Moid Riaz; Akyol, Ali; Rosigkeit, Henrik; Matzke, Linda; Grabenhorst, Isabel; Gómez, Jorge MarxThe 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.