Title page for ETD etd-11172008-144208


Type of Document Master's Thesis
Author Weekley, Jonathan Gardner
Author's Email Address jweekley@vt.edu
URN etd-11172008-144208
Title Multispectral Imaging Techniques for Monitoring Vegetative Growth and Health
Degree Master of Science
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Reinholtz, Charles F. Committee Co-Chair
Wicks, Alfred L. Committee Co-Chair
Meehan, Kathleen Committee Member
Nowak, Jerzy Committee Member
Keywords
  • Multispectral Imaging
  • Hyperspectral Imaging
  • Computer Vision
  • NDVI
Date of Defense 2007-12-04
Availability unrestricted
Abstract
Electromagnetic radiation reflectance increases dramatically around 700 nm for vegetation. This increase in reflectance is known as the vegetation red edge. The NDVI (Normalized Difference Vegetation index) is an imaging technique for quantifying red edge contrast for the identification of vegetation. This imaging technique relies on reflectance values for radiation with wavelength equal to 680 nm and 830 nm. The imaging systems required to obtain this precise reflectance data are commonly space-based; limiting the use of this technique due to satellite availability and cost.

This thesis presents a robust and inexpensive new terrestrial-based method for identifying the vegetation red edge. This new technique does not rely on precise wavelengths or narrow wavelength bands and instead applies the NDVI to the visible and NIR (near infrared) spectrums in toto.

The measurement of vegetation fluorescence has also been explored, as it is indirectly related to the efficiency of photochemistry and heat dissipation and provides a relative method for determining vegetation health.

The imaging methods presented in this thesis represent a unique solution for the real time monitoring of vegetation growth and senesces and the determination of qualitative vegetation health. A single, inexpensive system capable of field and greenhouse deployment has been developed. This system allows for the early detection of variations in plant growth and status, which will aid production of high quality horticultural crops.

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