The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.
A compilation of UAV applications for precision agriculture
Open accessAbstract
Climate change has introduced significant challenges that can affect multiple sectors, including the agricultural one. In particular, according to the Food and Agriculture Organization of the United Nations (FAO) and the International Telecommunication Union (ITU), the world population has to find new solutions to increase the food production by 70% by 2050. The answer to this crucial challenge is the suitable adoption and utilisation of the Information and Communications Technology (ICT) services, offering capabilities that can increase the productivity of the agrochemical products, such as pesticides and fertilisers and at the same time, they should minimise the functional cost. More detailed, the advent of the Internet of Things (IoT) and specifically, the rapid evolution of the Unmanned Aerial Vehicles (UAVs) and Wireless Sensor Networks (WSNs) can lead to valuable and at the same time economic Precision Agriculture (PA) applications, such as aerial crop monitoring and smart spraying tasks. In this paper, we provide a survey regarding the potential use of UAVs in PA, focusing on 20 relevant applications. More specifically, first, we provide a detailed overview of PA, by describing its various aspects and technologies, such as soil mapping and production mapping as well as the role of the Global Positioning Systems (GPS) and Geographical Information Systems (GIS). Then, we discriminate and analyse the various types of UAVs based on their technical characteristics and payload. Finally, we investigate in detail 20 UAV applications that are devoted to either aerial crop monitoring processes or spraying tasks. For each application, we examine the methodology adopted, the proposed UAV architecture, the UAV type, as well as the UAV technical characteristics and payload.
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Hasan, A. S. M. M., Sohel, F., Diepeveen, D., Laga, H., & Jones, M. G. K. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106067 · Cited by 478
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges
McEnroe, P., Wang, S., & Liyanage, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176400 · Cited by 482
Implementation of drone technology for farm monitoring & pesticide spraying: A review
Hafeez, A., Husain, M. A., Singh, S. P., Chauhan, A., Khan, M. T., Kumar, N., Chauhan, A., & Soni, S. K. (2023) · Information Processing in Agriculture · doi:10.1016/j.inpa.2022.02.002 · Cited by 268
A comparative study of deep learning and internet of things for precision agriculture
Saranya, T., Deisy, C., Sridevi, S., & Anbananthen, K. S. M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106034 · Cited by 232
A Review of GNSS-Independent UAV Navigation Techniques
Gyagenda, N., Hatilima, J. V., Roth, H., & Zhmud, V. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104069 · Cited by 190
Metadata
- Authors
- Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., & Moscholios, I.
- Venue
- Computer Networks
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 172
- Pages
- 107148
- ISSN
- 1389-1286 (ISSN-L)
- OpenAlex
- W3005863531
- Cited by
- 957
- References
- 108
- FWCI
- 70.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
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