TY - GEN
T1 - Drone-Based Data Collection for Precision Agriculture Applications
AU - Elhesasy, Mohamed
AU - Shashati, Mahmoud
AU - Alzeyoudi, Salem
AU - Alkhatib, Osama
AU - Hakim, Abdoalrahman
AU - Khasawneh, Adam
AU - Dief, Tarek N.
AU - Kamra, Mohamed M.
AU - Okasha, Mohamed
N1 - Publisher Copyright:
©2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Precision agriculture uses advanced technology to enhance farming methods and increase productivity. This study presents an innovative drone-based system integrated with a connected sensor network to autonomously collect data from low-power, wireless underground sensor nodes that monitor essential soil parameters. The system utilizes a drone equipped with an Xbee communication module to gather and store data on an onboard SD card for further analysis. Field tests demonstrated stable flight performance and reliable data transmission over distances up to 100 meters, with the drone maintaining an average speed of 5 meters per second. Sensor nodes were strategically deployed to ensure complete data coverage across the agricultural field. Optimized flight trajectories allowed the drone to cover up to 4500 meters in a 15-minute flight, which effectively monitors areas up to 40,000 square meters (4 hectares) per flight. Power consumption analysis indicated that the drone operates at approximately 100 watts per flight, while sensor nodes consume 0.1 watts due to their energy-efficient sleep mode. The integration of IoT technology in this drone-based system highlights its effectiveness for precision agriculture. This approach provides an autonomous, cost-effective, and user-friendly solution for data collection that enhances agricultural monitoring and management.
AB - Precision agriculture uses advanced technology to enhance farming methods and increase productivity. This study presents an innovative drone-based system integrated with a connected sensor network to autonomously collect data from low-power, wireless underground sensor nodes that monitor essential soil parameters. The system utilizes a drone equipped with an Xbee communication module to gather and store data on an onboard SD card for further analysis. Field tests demonstrated stable flight performance and reliable data transmission over distances up to 100 meters, with the drone maintaining an average speed of 5 meters per second. Sensor nodes were strategically deployed to ensure complete data coverage across the agricultural field. Optimized flight trajectories allowed the drone to cover up to 4500 meters in a 15-minute flight, which effectively monitors areas up to 40,000 square meters (4 hectares) per flight. Power consumption analysis indicated that the drone operates at approximately 100 watts per flight, while sensor nodes consume 0.1 watts due to their energy-efficient sleep mode. The integration of IoT technology in this drone-based system highlights its effectiveness for precision agriculture. This approach provides an autonomous, cost-effective, and user-friendly solution for data collection that enhances agricultural monitoring and management.
KW - drones
KW - IoT (Internet of Things)
KW - precision agriculture
KW - smart agriculture
KW - sustainability
UR - http://www.scopus.com/inward/record.url?scp=85217279094&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85217279094&partnerID=8YFLogxK
U2 - 10.1109/GCAIOT63427.2024.10833576
DO - 10.1109/GCAIOT63427.2024.10833576
M3 - Conference contribution
AN - SCOPUS:85217279094
T3 - 2024 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2024
BT - 2024 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2024
Y2 - 19 November 2024 through 21 November 2024
ER -