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 proceedingConference contribution

3 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 languageEnglish
Title of host publication2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728155326
DOIs
Publication statusPublished - Nov 2019
Event2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019 - Ras Al Khaimah, United Arab Emirates
Duration: Nov 19 2019Nov 21 2019

Publication series

Name2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019

Conference

Conference2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019
Country/TerritoryUnited Arab Emirates
CityRas Al Khaimah
Period11/19/1911/21/19

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

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