Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements and Challenges
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Abstract (arXiv)
In the next few years, smart farming will reach each and every nook of the world. The prospects of using unmanned aerial vehicles (UAV) for smart farming are immense. However, the cost and the ease in controlling UAVs for smart farming might play an important role for motivating farmers to use UAVs in farming. Mostly, UAVs are controlled by remote controllers using radio waves. There are several technologies such as WiFi or ZigBee that are also used for controlling UAVs. However, Smart Bluetooth (also referred to as Bluetooth Low Energy) is a wireless technology used to transfer data over short distances. Bluetooth smart is cheaper than other technologies and has the advantage of being available on every smart phone. Farmers can use any smart phone to operate their respective UAVs along with Bluetooth Smart enabled agricultural sensors in the future. However, certain requirements and challenges need to be addressed before UAVs can be operated for smart agriculture-related applications. Hence, in this article, an attempt has been made to explore the types of sensors suitable for smart farming, potential requirements and challenges for operating UAVs in smart agriculture. We have also identified the future applications of using UAVs in smart farming.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
6 papers.
- 2021Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVscited by 2
- 2022A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systemscited by 2
- 2022To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computingcited by 1
- 2023A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systemscited by 1
- 2026Toward Active Object Detection for UAVs in the Wild: A Large-Scale Dataset, Benchmark and Methodcited by 1
- 2025EIA-SEC: Improved Actor-Critic Framework for Multi-UAV Collaborative Control in Smart Agriculturecited by 0
- 2026Toward Active Object Detection for UAVs in the Wild: A Large-Scale Dataset, Benchmark and Method10 Jul 2026
- 2025EIA-SEC: Improved Actor-Critic Framework for Multi-UAV Collaborative Control in Smart Agriculture21 Dec 2025
- 2023A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems13 Jul 2023
- 2022To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing5 Sep 2022
- 2022A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems1 Apr 2022
- 2021Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs30 Nov 2021