An Integrated Precision Farming Application based on 5G, UAV and Deep Learning

Razaak, Manzoor and Kerdegari, Hamideh and Davies, Eleanor and Abozariba, Raouf and Broadbent, Matthew and Mason, Katy and Argyriou, Vasileios and Remagnino, Paolo (2019) An Integrated Precision Farming Application based on 5G, UAV and Deep Learning. In: International Conference on Computer Analysis of Images and Patterns, 23 August 2019, Salerno, Italy.

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Abstract

Wireless communication technology has made tremendous progress over the last two decades providing extensive coverage, high data-rate and low-latency. The current major upgrade, the fifth generation (5G) wireless technology promises substantial improvement over 4G broadband cellular technology. However, even in many developed countries, rural areas are significantly under-connected with mobile wireless technology. Developing 5G testbeds in rural areas can provide an incentive for service providers to improve internet connectivity. 5G Rural Integrated Testbed (5GRIT) is a project commissioned to develop testbeds for 5G in rural areas in the United Kingdom (UK). The project aims to demonstrate the role 5G networks can play in empowering farming and tourism sectors using an integrated system of unmanned aerial vehicles (UAV) and artificial intelligence technologies. This paper reports some of the studies and findings of the 5GRIT project, specifically, the results of testbed implementation and the deep learning algorithms developed for precision farming applications.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN: 9783030299309
Date: 23 August 2019
Uncontrolled Keywords: Unmanned aerial vehicles (UAV), Deep learning, 5G ,Precision farming, 5GRIT
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Raouf Abozariba
Date Deposited: 11 Feb 2020 12:09
Last Modified: 10 Sep 2020 05:30
URI: http://www.open-access.bcu.ac.uk/id/eprint/8875

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