Machine vision tracking of water advance in cotton surface irrigation with an unmanned aerial system

Long, Derek ORCID: https://orcid.org/0000-0001-9414-5064 (2018) Machine vision tracking of water advance in cotton surface irrigation with an unmanned aerial system. [Thesis (PhD/Research)]



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Item Type: Thesis (PhD/Research)
Item Status: Live Archive
Additional Information: Doctor of Philosophy (PhD) thesis. Access to this thesis permanently restricted at author's request.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Supervisors: McCarthy, Cheryl; Jensen, Troy
Date Deposited: 21 Jan 2019 01:44
Last Modified: 20 Nov 2020 04:19
Uncontrolled Keywords: cotton surface irrigation, thermal imagery, image analysis
Fields of Research (2008): 07 Agricultural and Veterinary Sciences > 0799 Other Agricultural and Veterinary Sciences > 079901 Agricultural Hydrology (Drainage, Flooding, Irrigation, Quality, etc.)
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Fields of Research (2020): 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300201 Agricultural hydrology
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing
Identification Number or DOI: doi:10.26192/5f643ba5ce22b
URI: http://eprints.usq.edu.au/id/eprint/35499

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