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Proceedings Paper

Comprehensive UAV agricultural remote-sensing research at Texas A and M University
Author(s): J. Alex Thomasson; Yeyin Shi; Jeffrey Olsenholler; John Valasek; Seth C. Murray; Michael P. Bishop
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Paper Abstract

Unmanned aerial vehicles (UAVs) have advantages over manned vehicles for agricultural remote sensing. Flying UAVs is less expensive, is more flexible in scheduling, enables lower altitudes, uses lower speeds, and provides better spatial resolution for imaging. The main disadvantage is that, at lower altitudes and speeds, only small areas can be imaged. However, on large farms with contiguous fields, high-quality images can be collected regularly by using UAVs with appropriate sensing technologies that enable high-quality image mosaics to be created with sufficient metadata and ground-control points. In the United States, rules governing the use of aircraft are promulgated and enforced by the Federal Aviation Administration (FAA), and rules governing UAVs are currently in flux. Operators must apply for appropriate permissions to fly UAVs. In the summer of 2015 Texas A&M University's agricultural research agency, Texas A&M AgriLife Research, embarked on a comprehensive program of remote sensing with UAVs at its 568-ha Brazos Bottom Research Farm. This farm is made up of numerous fields where various crops are grown in plots or complete fields. The crops include cotton, corn, sorghum, and wheat. After gaining FAA permission to fly at the farm, the research team used multiple fixed-wing and rotary-wing UAVs along with various sensors to collect images over all parts of the farm at least once per week. This article reports on details of flight operations and sensing and analysis protocols, and it includes some lessons learned in the process of developing a UAV remote-sensing effort of this sort.

Paper Details

Date Published: 17 May 2016
PDF: 7 pages
Proc. SPIE 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 986602 (17 May 2016); doi: 10.1117/12.2234052
Show Author Affiliations
J. Alex Thomasson, Texas A&M Univ. (United States)
Yeyin Shi, Texas A&M Univ. (United States)
Jeffrey Olsenholler, Texas A&M Univ. (United States)
John Valasek, Texas A&M Univ. (United States)
Seth C. Murray, Texas A&M Univ. (United States)
Michael P. Bishop, Texas A&M Univ. (United States)

Published in SPIE Proceedings Vol. 9866:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping
John Valasek; J. Alex Thomasson, Editor(s)

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