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Proceedings Paper

Automatic detection and counting of cattle in UAV imagery based on machine vision technology (Conference Presentation)
Author(s): Maryam Rahnemoonfar; Jamie Foster; Michael J. Starek

Paper Abstract

Beef production is the main agricultural industry in Texas, and livestock are managed in pasture and rangeland which are usually huge in size, and are not easily accessible by vehicles. The current research method for livestock location identification and counting is visual observation which is very time consuming and costly. For animals on large tracts of land, manned aircraft may be necessary to count animals which is noisy and disturbs the animals, and may introduce a source of error in counts. Such manual approaches are expensive, slow and labor intensive. In this paper we study the combination of small unmanned aerial vehicle (sUAV) and machine vision technology as a valuable solution to manual animal surveying. A fixed-wing UAV fitted with GPS and digital RGB camera for photogrammetry was flown at the Welder Wildlife Foundation in Sinton, TX. Over 600 acres were flown with four UAS flights and individual photographs used to develop orthomosaic imagery. To detect animals in UAV imagery, a fully automatic technique was developed based on spatial and spectral characteristics of objects. This automatic technique can even detect small animals that are partially occluded by bushes. Experimental results in comparison to ground-truth show the effectiveness of our algorithm.

Paper Details

Date Published: 6 June 2017
PDF: 1 pages
Proc. SPIE 10217, Sensing for Agriculture and Food Quality and Safety IX, 102170J (6 June 2017); doi: 10.1117/12.2262830
Show Author Affiliations
Maryam Rahnemoonfar, Texas A&M Univ. Corpus Christi (United States)
Jamie Foster, Texas A&M AgriLife Research (United States)
Michael J. Starek, Texas A&M Univ. Corpus Christi (United States)


Published in SPIE Proceedings Vol. 10217:
Sensing for Agriculture and Food Quality and Safety IX
Moon S. Kim; Kuanglin Chao; Bryan A. Chin; Byoung-Kwan Cho, Editor(s)

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