Unmanned Arial Vehicle (UAV) Imagery and Manual Sampling for Parasitic Weed Recognition and Measurements

Eihab Fathelrahman; Elke Neumann; Mousa Hussein; Ahmad Jalil; Fatima Hassan; Ahmed Dirir; Safdar Muhammad

doi:10.1109/ICECTA48151.2019.8959613

Unmanned Arial Vehicle (UAV) Imagery and Manual Sampling for Parasitic Weed Recognition and Measurements

Eihab Fathelrahman, Elke Neumann, Mousa Hussein, Ahmad Jalil, Fatima Hassan, Ahmed Dirir, Safdar Muhammad

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

This research uses Unmanned Arial Vehicle (UAV) imagery and manual samplings methods simultaneously to recognize parasitic weed at Alfalfa stand. The objective is to identify the best techniques to monitor parasitic weed infestation. Imagery was digitized using GIS software to measure weed spatial distribution across four strips. Advantage and limitations of using the drone to the arid land conditions are discussed. Results indicated UAV, manual sampling, and GIS software methods are complemented rather than substituted to each other. The integration of the UAV and GIS components produces a feasible option against costly complex multi-spectrum camera systems for parasitic weed recognition. The proposed system enables early weed recognition for farmers to enable further parasitic weed management.

Original language	English
Title of host publication	2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728155326
DOIs	https://doi.org/10.1109/ICECTA48151.2019.8959613
Publication status	Published - Nov 2019
Event	2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019 - Ras Al Khaimah, United Arab Emirates Duration: Nov 19 2019 → Nov 21 2019

Publication series

Name	2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019

Conference

Conference	2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019
Country/Territory	United Arab Emirates
City	Ras Al Khaimah
Period	11/19/19 → 11/21/19

Keywords

Forage Management
GIS
Parasitic Weed Recognition
UAV Imagery

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Software
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Computer Networks and Communications

Access to Document

10.1109/ICECTA48151.2019.8959613

Cite this

Fathelrahman, E., Neumann, E., Hussein, M., Jalil, A., Hassan, F., Dirir, A., & Muhammad, S. (2019). Unmanned Arial Vehicle (UAV) Imagery and Manual Sampling for Parasitic Weed Recognition and Measurements. In 2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019 Article 8959613 (2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECTA48151.2019.8959613

Unmanned Arial Vehicle (UAV) Imagery and Manual Sampling for Parasitic Weed Recognition and Measurements. / Fathelrahman, Eihab ; Neumann, Elke ; Hussein, Mousa et al.
2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8959613 (2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fathelrahman, E , Neumann, E , Hussein, M, Jalil, A, Hassan, F, Dirir, A & Muhammad, S 2019, Unmanned Arial Vehicle (UAV) Imagery and Manual Sampling for Parasitic Weed Recognition and Measurements. in 2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019., 8959613, 2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019, Institute of Electrical and Electronics Engineers Inc., 2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019, Ras Al Khaimah, United Arab Emirates, 11/19/19. https://doi.org/10.1109/ICECTA48151.2019.8959613

Fathelrahman E , Neumann E , Hussein M, Jalil A, Hassan F, Dirir A et al. Unmanned Arial Vehicle (UAV) Imagery and Manual Sampling for Parasitic Weed Recognition and Measurements. In 2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8959613. (2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019). doi: 10.1109/ICECTA48151.2019.8959613

Fathelrahman, Eihab ; Neumann, Elke ; Hussein, Mousa et al. / Unmanned Arial Vehicle (UAV) Imagery and Manual Sampling for Parasitic Weed Recognition and Measurements. 2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019).

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abstract = "This research uses Unmanned Arial Vehicle (UAV) imagery and manual samplings methods simultaneously to recognize parasitic weed at Alfalfa stand. The objective is to identify the best techniques to monitor parasitic weed infestation. Imagery was digitized using GIS software to measure weed spatial distribution across four strips. Advantage and limitations of using the drone to the arid land conditions are discussed. Results indicated UAV, manual sampling, and GIS software methods are complemented rather than substituted to each other. The integration of the UAV and GIS components produces a feasible option against costly complex multi-spectrum camera systems for parasitic weed recognition. The proposed system enables early weed recognition for farmers to enable further parasitic weed management.",

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AU - Hassan, Fatima

AU - Dirir, Ahmed

AU - Muhammad, Safdar

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Unmanned Arial Vehicle (UAV) Imagery and Manual Sampling for Parasitic Weed Recognition and Measurements

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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