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Proceedings Paper

Impact of camera lens angle and sUAS flying altitude on spatial crop canopy temperature evaluation
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Paper Abstract

Thermal image quality is critical to accurately quantify spatial and temporal growth and stress patterns of field crops. Image quality can be impacted by several factor including environment, flying altitude, and camera focal length. Often times the thermal sensor selection is based upon price or already owned sensor. Metrics are available to select the flight altitude based on thermal sensor for desired ground resolution, however no study have been conducted to provide relative difference in image quality and efficiency of generating a thermal orthomosaic. Therefore, this study was conducted with goal to compare accuracy of canopy temperature quantification and assess the quality of thermal orthomosaic when using thermal sensor of different focal length and image acquisition at varying flying altitudes of a sUAS. Three thermal infrared cameras were selected with focal lengths of 9mm, 13mm, and 19mm. All three cameras were flown at altitudes of 20m, 50m, and 80m, to collect aerial imagery of 7,000 m2 soybeans field. The cameras were mounted on a rotary quadcopter. All flights were conducted at 3 m/s flying speed, and 1 second shutter trigger interval. A ground reference system provided ground truth data for thermometric transformations. Imagery data was compared to assess differences in number of images collected, percentage overlap required for 1 second shutter trigger interval, quality of orthomosaic and accuracy of canopy temperatures. Results show that 13 mm focal length and 50 m altitude result in a finer resolution orthomosaic. The canopy temperatures were quantified accurately regardless of altitude and focal length.

Paper Details

Date Published: 14 May 2019
PDF: 11 pages
Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 1100805 (14 May 2019); doi: 10.1117/12.2519378
Show Author Affiliations
Harman S. Sangha, Kansas State Univ. (United States)
Ajay Sharda, Kansas State Univ. (United States)
Lukas Koch, Kansas State Univ. (United States)
Guanghui Wang, The Univ. of Kansas (United States)
Pavithra Prabhakar, Kansas State Univ. (United States)

Published in SPIE Proceedings Vol. 11008:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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