Date of Award:

5-2016

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Civil and Environmental Engineering

Committee Chair(s)

Mac McKee

Committee

Mac McKee

Committee

Niel Allen

Committee

David Stevens

Committee

Douglas Ramsey

Committee

David Rosenberg

Abstract

Precision agriculture (PA) is an integration of a set of technologies aiming to improve productivity and profitability while sustaining the quality of the surrounding environment. It is a process that vastly relies on high-resolution information to enable greater precision in the management of inputs to production. This dissertation explores the usage of multispectral high resolution aerial imagery acquired by an unmanned aerial systems (UAS) platform to estimate three fundamental crop parameters (plant chlorophyll, leaf nitrogen and crop water demand). The UAS acquired imagery in the visual, near infrared and thermal infrared spectra with a resolution of less than a meter (15 - 60 cm). This research applied a well-established machine learning algorithm (relevance vector machine) to the five band imagery (R, G, B, NIR, and TIR) to estimate plant chlorophyll content and leaf nitrogen respectively. In addition, this research explored the usage of the high resolution imagery to estimate crop evapotranspiration (ET) at 15 cm resolution. A comparison was also made between the high resolution ET and Landsat derived ET over two different crop cover (field crops and vineyards) to assess the advantages of UAS based high resolution ET. This research aims to bridge the information embedded in the high resolution imagery with ground crop parameters to provide site specific information to assist farmers adopting precision agriculture.

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