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Proceedings Paper

Detection of wine grape nutrient levels using visible and near infrared 1nm spectral resolution remote sensing
Author(s): Grant Anderson; Jan van Aardt; Peter Bajorski; Justine Vanden Heuvel
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Paper Abstract

The grape industry relies on regular crop assessment to aid in the day-to-day and seasonal management of their crop. More specifically, there are six key nutrients of interest to viticulturists in the growing of wine grapes, namely 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. We collected ground-level observations of the spectra of the grape vines, using a hyperspectral spectrometer (0.4–2.5um), at the same time that petioles samples were harvested. We then interpolated the data into a consistent 1 nm spectral resolution before comparing it to the nutrient data collected. This nutrient data came from both the industry standard petiole analysis, as well as an additional leaf-level analysis. The data were collected for two different grape cultivars, both during bloom and veraison periods to provide variability, while also considering the impact of temporal/seasonal change. A narrow-band NDI (Normalized Difference Index) approach, as well as a simple ratio index, was used to determine the correlation of the reflectance data to the nutrient data. This analysis was limited to the silicon photodiode range to increase the utility of our approach for wavelength-specific cameras (via spectral filters) in a low cost drone platform. The NDI generated correlation coefficients were as high as 0.80 and 0.88 for bloom and veraison, respectively. The ratio index produced correlation coefficient results that are the same at two decimal places with 0.80 and 0.88. These results bode well for eventual non-destructive, accurate and precise assessment of vineyard nutrient status.

Paper Details

Date Published: 17 May 2016
PDF: 14 pages
Proc. SPIE 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 98660H (17 May 2016); doi: 10.1117/12.2227720
Show Author Affiliations
Grant Anderson, Rochester Institute of Technology (United States)
Jan van Aardt, Rochester Institute of Technology (United States)
Peter Bajorski, Rochester Institute of Technology (United States)
Justine Vanden Heuvel, Cornell Univ. (United States)

Published in SPIE Proceedings Vol. 9866:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping
John Valasek; J. Alex Thomasson, Editor(s)

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