The grape industry relies on in situ crop assessment to aid in the day-to-day and seasonal management of their crop. In the case of soil-plant chemistry interactions, there are six key nutrients of interest to viticulturists in the growing of wine grapes: nitrogen, potassium, phosphorous, magnesium, zinc, and boron. Traditional methods of determining the levels of these nutrients are through collection and chemical analysis of petiole samples from the grape vines themselves. In this study, however, we collected ground-level observations of the spectra of the grape vines using a hyperspectral spectroradiometer (0.4-2.5µm range; 1nm resampled spectral interval) at the same time that petioles samples were harvested. The data were collected for two different grape cultivars, both during bloom and veraison phenological stages to provide analytical variability, while also considering the impact of temporal/seasonal change. The data were interpolated to 1nm bandwidths, yielding a consistent 1nm spectral resolution before comparing it to the nutrient data collected. Spectral reflectance also was resampled to match the 10nm bands used by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS); this was done to assess the efficacy of nutrient modeling using a more standard, airborne system’s spectral resolution. Our analysis was limited to the silicon photodiode range to increase the utility of the approach for wavelength-specific cameras (via spectral filters) in a low cost unmanned aerial vehicle (UAV) platform. Five different approaches were tested to fit the data to the nutrient data. These were: a narrow-band Normalized Difference Index (NDI) approach using a standard linear fit, step-wise linear regression (SLR) using the silicon range of wavelengths, SLR using the NDI that correlated highly with the nutrient data, SLR using the 1st derivative of the reflectance spectra, and SLR using continuum-removed spectra, applied over the red trough (560-750nm) spectral region. For 1nm reflectance data, these methods generated models for nutrient modeling using between 2-10 wavelengths, and associated coefficients of determination values ranging between R2 = 0.74-0.86 across the six nutrients. In the case of the 10nm resampled spectral data, model fits ranged between R2 = 0.61-0.93 across the six nutrients, using 2-18 unique wavelength bands. These results bode well for eventual non-destructive, accurate and precise assessment of vineyard nutrient status through the use of UAVs.

Library of Congress Subject Headings

Grapes--Remote sensing; Plant nutrients--Remote sensing; Multispectral imaging; Remote sensing--Data processing

Publication Date


Document Type


Student Type


Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


Jan A. van Aardt

Advisor/Committee Member

Peter Bajorski

Advisor/Committee Member

Carl Salvaggio


Physical copy available from RIT's Wallace Library at SB388 .A64 2016


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