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Cited by 612

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications

Open access
Kim, Jeongeun, Kim, Seungwon, Ju, Chanyoung, Son, Hyoung Il·2019·IEEE Access

Abstract

For agricultural applications, regularized smart-farming solutions are being considered, including the use of unmanned aerial vehicles (UAVs). The UAVs combine information and communication technologies, robots, artificial intelligence, big data, and the Internet of Things. The agricultural UAVs are highly capable, and their use has expanded across all areas of agriculture, including pesticide and fertilizer spraying, seed sowing, and growth assessment and mapping. Accordingly, the market for agricultural UAVs is expected to continue growing with the related technologies. In this study, we consider the latest trends and applications of leading technologies related to agricultural UAVs, control technologies, equipment, and development. We discuss the use of UAVs in real agricultural environments. Furthermore, the future development of the agricultural UAVs and their challenges are presented.

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Metadata

Authors
Kim, J., Kim, S., Ju, C., & Son, H. I.
Venue
IEEE Access
Published
2019
Volume
7
Pages
105100-105115
ISSN
2169-3536 (ISSN-L)
OpenAlex
W2966657623
Cited by
612
References
3
FWCI
34.00
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications. https://app.cochat.ai/papers/10.1109/access.2019.2932119
The paper
Kim, J., Kim, S., Ju, C., & Son, H. I. (2019). Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications. IEEE Access. https://doi.org/10.1109/access.2019.2932119

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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.