The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Drones in agriculture: A review and bibliometric analysis
Open accessAbstract
Drones, also called Unmanned Aerial Vehicles (UAV), have witnessed a remarkable development in recent decades. In agriculture, they have changed farming practices by offering farmers substantial cost savings, increased operational efficiency, and better profitability. Over the past decades, the topic of agricultural drones has attracted remarkable academic attention. We therefore conduct a comprehensive review based on bibliometrics to summarize and structure existing academic literature and reveal current research trends and hotspots. We apply bibliometric techniques and analyze the literature surrounding agricultural drones to summarize and assess previous research. Our analysis indicates that remote sensing, precision agriculture, deep learning, machine learning, and the Internet of Things are critical topics related to agricultural drones. The co-citation analysis reveals six broad research clusters in the literature. This study is one of the first attempts to summarize drone research in agriculture and suggest future research directions.
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Cites
Software survey: VOSviewer, a computer program for bibliometric mapping
van Eck, N. J., & Waltman, L. (2010) · Scientometrics · doi:10.1007/s11192-009-0146-3 · Cited by 21,484
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
Science, technology and the future of small autonomous drones
Floreano, D., & Wood, R. J. (2015) · Nature · doi:10.1038/nature14542 · Cited by 1,476
Baldi, S. (1998) · American Sociological Review · doi:10.2307/2657504 · Cited by 335
IoT Based Agriculture as a Cloud and Big Data Service: The Beginning of Digital India
Gill, S. S., Chana, I., & Buyya, R. (2017) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.2017100101 · Cited by 125
A bibliographic study on big data: concepts, trends and challenges
Mishra, D., Luo, Z., Jiang, S., Papadopoulos, T., & Dubey, R. (2017) · Business Process Management Journal · doi:10.1108/bpmj-10-2015-0149 · Cited by 84
Cited by
The Internet of Things (IoT) in healthcare: Taking stock and moving forward
Rejeb, A., Rejeb, K., Treiblmaier, H., Appolloni, A., Alghamdi, S., Alhasawi, Y., & Iranmanesh, M. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100721 · Cited by 448
Self-citationBadgujar, C. M., Poulose, A., & Gan, H. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109090 · Cited by 411
Guebsi, R., Mami, S., & Chokmani, K. (2024) · Drones · doi:10.3390/drones8110686 · Cited by 294
Herich, D., & Vaščák, J. (2024) · Drones · doi:10.3390/drones8020034 · Cited by 14
Metadata
- Authors
- Rejeb, A., Abdollahi, A., Rejeb, K., & Treiblmaier, H.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 198
- Pages
- 107017
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W4280610169
- Cited by
- 701
- References
- 6
- FWCI
- 67.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Rejeb, A., Abdollahi, A., Rejeb, K., & Treiblmaier, H. (2022). Drones in agriculture: A review and bibliometric analysis. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.107017
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
