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An initial analysis of real-time sUAS-based detection of grapevine water status in the Finger Lakes wine country of upstate New York
Author(s): Rinaldo R. Izzo; Alan N. Lakso; Evan D. Marcellus; Timothy D. Bauch; Nina G. Raqueño; Jan van Aardt
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Paper Abstract

The quality of grapes in the production of wine is highly influenced by vine water status, where optimal water deficit or selective harvesting can improve berry quality. It is in this context that the rapid advancement in small unmanned aerial system (sUAS) technology and the potential application of real-time, high-spatial resolution hyperspectral imagery for vineyard moisture assessment, have become tractable. This study sought to further sUAS hyperspectral imagery as a tool to model water status in a commercial vineyard in Upstate New York. High-spatial resolution (2.5 cm ground sample distance) hyperspectral data were collected in the visible/near-infrared (VNIR; 400-1000nm) regime on three flight days. A Scholander pressure chamber was used to directly measure the midday stem water potential (Ψstem) within imaged vines at the time of flight. High spatial resolution pixels enabled the targeting of pure (sunlit) vine canopy with vertically trained shoots and significant shadowing. We used the partial least squares-regression (PLS-R) modeling method to correlate our hyperspectral imagery with measured field water status and applied a wavelength band selection scheme to detect important wavelengths. We evaluated spectral smoothing and band reduction approaches, given signal-to-noise ratio (SNR) concerns. Our regression results indicated that unsmoothed curves, with the range of wavelength bands from 450- 1000 nm, provided the highest model performance with R2 = 0.68 for cross-validation. Future work will include hyperspectral flight data in the short-wave infrared (SWIR; 1000-2500 nm) regime that were also collected. Ultimately, models will need validation in different vineyards with a full range of plant stress.

Paper Details

Date Published: 14 May 2019
PDF: 18 pages
Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 1100811 (14 May 2019); doi: 10.1117/12.2518762
Show Author Affiliations
Rinaldo R. Izzo, Rochester Institute of Technology (United States)
Alan N. Lakso, Cornell Univ. (United States)
Evan D. Marcellus, Rochester Institute of Technology (United States)
Timothy D. Bauch, Rochester Institute of Technology (United States)
Nina G. Raqueño, Rochester Institute of Technology (United States)
Jan van Aardt, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 11008:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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