Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize
Abstract
:1. Introduction
2. Materials and Methods
2.1. Proposed UAS Data Ferrying Designs
2.1.1. Criteria
2.1.2. Constraints
2.1.3. Design Parameters and Proposed UAS Data Ferrying Designs
- Three wireless technologies designed for industrial/commercial applications that are mostly used for agricultural farm operations were explored for this study: long-range radio (LoRa), ZigBee, and general packet radio service (GPRS). The LoRa protocol was introduced by the LoRa Alliance for the low power and wide-area Internet of Things (IoT) communication associated with the indoor transmission, Pitì et al. [24]. The LoRa gateway can collect data from LoRa nodes to construct the topology of a star network and may communicate with a cloud server over a long communication for high scalability. The LoRa protocol has a wide range of applicability in precision agriculture [6,25,26]. Sensor nodes based on the ZigBee wireless protocol in the agricultural field can communicate with a router up to 100 m. Recent studies have employed ZigBee for precision agriculture [27,28,29] because of its low power consumption, low cost, self-forming characteristics, and suitable communication range. General packet radio service (GPRS)/3G/4G employs packet data (a bit of data that is packaged for transmission) service for GSM-based cellular phones. The GPRS technology communication rates depend on consumer volume where consumers share common communication channels and resources and frequently experience variable delays and throughputs. The sensors could be interfaced to the GPRS system sensor board to obtain and transmit information to the remote server through the GPRS board, which depends on a GSM/GPRS mobile network. Some studies have deployed a GSM/GPRS mobile network for applications in precision agriculture [30,31,32].
- Fixed-wing and multi-rotor UAS designs were considered as these are mostly used for agricultural operations. Fixed-wing UAS has a longer flight endurance capacity while multi-rotors can provide for stable and easy vertical take-off and landing [33].
- The power supply to the wireless communication setup on the UAS could be tethered from the UAS or be powered from an external battery source mounted on the UAS.
- Transmitted data could be stored on the memory storage of the UAS or transmitted to the cloud.
2.1.4. Decision Matrix
2.2. Field Experiment Description
2.3. Sensors on the UAS Data Ferry and Node Stations
2.3.1. UAS Data Ferry Sensors
RF 450 Radio
Dipole Omnidirectional Antenna
Wave Omnidirectional Antenna
2.3.2. Node Station Sensors
CR1000X Datalogger
GS-1 Soil Water Sensor
SI-111 Infrared Radiometer
2.3.3. Unmanned Aerial System (UAS)
Matrice 600 Pro Hexacopter
2.4. Experiment Design
3. Results
4. Discussion
5. Summary and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Criteria 1 Wireless Technology | Criteria 2 UAS | Criteria 3 Power Supply | Criteria 4 Data Storage | Constraint 1 Cost | Constraint 2 Adoptability | Total | |
---|---|---|---|---|---|---|---|
Design A | 2 | 1 | 1 | 1 | 3 | 3 | 11 |
Design B | 3 | 2 | 2 | 2 | 4 | 5 | 18 |
Design C | 1 | 1 | 2 | 2 | 4 | 3 | 13 |
Design D | 2 | 2 | 1 | 1 | 5 | 5 | 16 |
Design E | 3 | 1 | 1 | 1 | 3 | 3 | 12 |
Design F | 1 | 2 | 2 | 2 | 5 | 4 | 16 |
Maize Growing Season | Planting Date | Vegetative Period | Reproductive Period |
---|---|---|---|
2020 | 11 May | 28 May–16 July | 17 July–29 September |
2021 | 28 April | 14 May–16 July | 17 July–26 September |
2020 Growing Season | 2021 Growing Season | ||
---|---|---|---|
Flight Date | Physiological Growth Stage | Flight Date | Physiological Growth Stage |
September 2 | R5.3 | July 27 | R1 (silking) |
September 16 | R5.75 | July 30 | R2 (early blister) |
September 29 | R6 | August 21 | R5.1 (early dent) |
August 24 | R5.2 | ||
August 25 | R5.25 | ||
September 4 | R5.4 | ||
September 6 | R5.5 | ||
September 8 | R5.55 | ||
September 10 | R5.6 |
CSR (in %) during 2020 Growing Season in Maize | |||
---|---|---|---|
Flight Date | 31 m above Ground | 61 m above Ground | 122 m above Ground |
September 2 | 100% | - | 25% |
September 16 | 100% | 50% | 50% |
September 30 | 100% | 75% | 75% |
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Singh, J.; Ge, Y.; Heeren, D.M.; Walter-Shea, E.; Neale, C.M.U.; Irmak, S.; Maguire, M.S. Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize. Sensors 2022, 22, 1863. https://doi.org/10.3390/s22051863
Singh J, Ge Y, Heeren DM, Walter-Shea E, Neale CMU, Irmak S, Maguire MS. Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize. Sensors. 2022; 22(5):1863. https://doi.org/10.3390/s22051863
Chicago/Turabian StyleSingh, Jasreman, Yufeng Ge, Derek M. Heeren, Elizabeth Walter-Shea, Christopher M. U. Neale, Suat Irmak, and Mitchell S. Maguire. 2022. "Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize" Sensors 22, no. 5: 1863. https://doi.org/10.3390/s22051863
APA StyleSingh, J., Ge, Y., Heeren, D. M., Walter-Shea, E., Neale, C. M. U., Irmak, S., & Maguire, M. S. (2022). Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize. Sensors, 22(5), 1863. https://doi.org/10.3390/s22051863