An integrated precision farming application based on 5G, UAV and deep learning technologies

Razaak, Manzoor, Kerdegari, Hamideh, Davies, Eleanor, Abozariba, Raouf, Broadbent, Matthew, Mason, Katy, Argyriou, Vasileios and Remagnino, Paolo (2019) An integrated precision farming application based on 5G, UAV and deep learning technologies. In: CAIP 2019: International Conference on Computer Analysis of Images and Patterns; 06 Sep 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)
Event Title: CAIP 2019: International Conference on Computer Analysis of Images and Patterns
Additional Information: Published in: Vento, Mario, Percannella, Gennaro, Colantonio, Sara, Giorgi, Daniela, Matuszewski, Bogdan J., Kerdegari, Hamideh and Razaak, Manzoor, (eds.) (2019) Computer Analysis of Images and Patterns : CAIP 2019 International Workshops, ViMaBi and DL-UAV. Cham, Switzerland : Springer International Publishing. pp. 109-119. (Communications in Computer and Information Science, no. 1089) ISSN (print) 1865-0929 ISBN 9783030299293.
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing
Depositing User: Philip Keates
Date Deposited: 26 Feb 2020 11:04
Last Modified: 26 Feb 2020 11:30
DOI: https://doi.org/10.1007/978-3-030-29930-9_11
URI: http://eprints.kingston.ac.uk/id/eprint/44212

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