Author ORCID Identifier

https://orcid.org/0000-0002-2978-5679

Year of Publication

2020

Degree Name

Master of Science in Biosystems and Agricultural Engineering (MSBiosyAgE)

Document Type

Master's Thesis

College

Agriculture; Engineering

Department

Biosystems and Agricultural Engineering

First Advisor

Dr. Michael Sama

Abstract

The overall objective of this study was to evaluate the use of consumer-grade unmanned aircraft systems for image-based remote sensing in agriculture with application towards livestock health monitoring. A two-dimensional spatial error experiment was conducted to quantify the spatial accuracy of georeferenced orthomosaic imagery collected using a drone and processed with photogrammetry software. Treatment variables included altitude above ground level and image data type (visible and multispectral). The results from the ANOVA test indicated that there were significant differences between data types, but no significant differences between altitudes. The experiment was then expanded to a three-dimensional study where two life-size cow statues were extensively photographed and processed in groupings to simulate different flights formations. The resulting volume estimations from groups of images were compared to the estimate when using all images. Results revealed the importance of selecting the right flight paths to produce the most c 3D model.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2020.337

Funding Information

From 2017-2019

This work was partially supported by the US Department of Agriculture under Grant No. 2018-67021-27416, NRI: INT: Autonomous Unmanned Aerial Robots for Livestock Heath Monitoring

This work was partially supported by the National Science Foundation under Grant No. 1539070, Collaboration Leading Operational UAS Development for Meteorology and Atmospheric Physics (CLOUD-MAP) to Oklahoma State University in partnership with University of Oklahoma, University of Nebraska-Lincoln, and the University of Kentucky.

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