Abstract

Remote sensing applications in agriculture are established for large-scale monitoring with satellite and airborne imagery, but unmanned aerial systems (UAS) are poised to bring in-field mapping capabilities to the hands of individual farmers. UAS imaging holds several advantages over traditional methods, including centimeter-scale resolution, reduced atmospheric absorption, flexible timing of data acquisitions, and ease of use. In this work, we present two studies using UAS imaging of specialty crops in upstate New York to work towards improved crop management applications. The first study is an investigation of multispectral imagery obtained over table beet fields in Batavia, NY during the 2018 and 2019 seasons to be used in root yield modeling. We determined optimal growth stages for future observations and establish the importance of quantifying early growth via determination of canopy area, a feature unattainable with lower resolution imaging. We developed models for root mass and count based on area-augmented imagery of our raw study plots and their corresponding ground truth data for practical testing with independent data sets. The second study was designed to determine an optimal subset of wavelengths derived from hyperspectral imagery that are related to grapevine nutrients for improved vineyard nutrient monitoring. Our ensemble wavelength selection and regression algorithm chose wavelengths consistent with known absorption features related to nitrogen content in vegetation. Our model achieved a leave-one-out cross-validation root-mean-squared error of 0.17% nitrogen in our dried vine-leaf samples with 2.4-3.6% nitrogen. This is an improvement upon published studies of typical UAS multispectral sensors used to assess grapevine nitrogen status. With further testing on new data, we can determine consistently selected wavelengths and guide the design of specialty multispectral sensors for improved grapevine nutrient management.

Library of Congress Subject Headings

Crops--Remote sensing; Drone aircraft; Multispectral imaging

Publication Date

12-2021

Document Type

Thesis

Student Type

Graduate

Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Jan van Aardt

Advisor/Committee Member

Anthony Vodacek

Advisor/Committee Member

Terry Bates

Campus

RIT – Main Campus

Plan Codes

IMGS-MS

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