Doctoral Dissertations

Date of Award

12-2019

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Biosystems Engineering

Major Professor

Michael Buschermohle, John Wilkerson

Committee Members

William Hart, Tyson Raper

Abstract

The use of unmanned aerial systems (UAS) in production agriculture has exponentially increased in recent years due to their ability to acquire aerial imagery in a time and cost effective manner. Many producers and crop advisors use UAS as a platform to collect multispectral imagery that can be used to calculate a vegetation index (VI) map. These VI maps are then used to assess crop health and to make in-season management decisions on a spatial basis. Due to the surge in UAS adoption by non-experienced users as well as the sensitivity of potential management decisions to data quality, the first objective of this dissertation is to identify environmental attributes that impact the stability of VI maps derived from UAS-acquired data and develop recommendations for standard operating procedures. Specifically, these environmental factors include diurnal effects as well as shading from transient cloud cover. Analysis revealed that shadows cast by clouds can have a significant effect on normalized difference VIs, however these differences are not great enough to warrant a management change. However, the reflectance in individual bands is consistently and significantly affected by cloud cover. Also, a significant diurnal effect was observed in VIs and in the individual wavelengths. In order to fully realize benefits of low-cost technologies such as UAS and smartphones, the second objective is to identify image acquisition and post-processing techniques to enhance the utility of VI maps derived from smartphone and UAS-acquired data. Specifically, the objectives of this study were to evaluate the ability of early and late season ground-based measurements to provide insight into cotton nitrogen (N) status and to evaluate the ability of aerial-based measurements to correlate to ground-based measurements. This study demonstrates the usefulness of VIs developed from both UAS and smartphones in predicting in-season cotton N status.

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