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.
The research leading to these results have received funding from the Department for Digital, Culture, Media & Sports (DCMS), United Kingdom, under its 5G trials and testbeds program.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
5G Rural Integrated Testbed. http://www.5grit.co.uk/. Accessed 01 July 2019
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Mitola, J.: Accelerating 5G QoE via public-private spectrum sharing. IEEE Commun. Mag. 52(5), 77–85 (2014)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Sa, I., et al.: weedNet: dense semantic weed classification using multispectral images and MAV for smart farming. IEEE Robot. Autom. Lett. 3(1), 588–595 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Razaak, M. et al. (2019). An Integrated Precision Farming Application Based on 5G, UAV and Deep Learning Technologies. In: Vento, M., et al. Computer Analysis of Images and Patterns. CAIP 2019. Communications in Computer and Information Science, vol 1089. Springer, Cham. https://doi.org/10.1007/978-3-030-29930-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-29930-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29929-3
Online ISBN: 978-3-030-29930-9
eBook Packages: Computer ScienceComputer Science (R0)