An initial analysis of real-time sUAS-based detection of grapevine water status in the Finger Lakes wine country of upstate New York

Rinaldo R. Izzo; Alan N. Lakso; Evan D. Marcellus; Timothy D. Bauch; Nina G. Raqueño; Jan van Aardt

doi:10.1117/12.2518762

14 May 2019 An initial analysis of real-time sUAS-based detection of grapevine water status in the Finger Lakes wine country of upstate New York

Rinaldo R. Izzo, Alan N. Lakso, Evan D. Marcellus, Timothy D. Bauch, Nina G. Raqueño, Jan van Aardt

Author Affiliations +

Proceedings Volume 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV; 1100811 (2019) https://doi.org/10.1117/12.2518762
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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 (Ψ_stem) 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.

Conference Presentation

Citation Download Citation

Rinaldo R. Izzo, Alan N. Lakso, Evan D. Marcellus, Timothy D. Bauch, Nina G. Raqueño, and Jan van Aardt "An initial analysis of real-time sUAS-based detection of grapevine water status in the Finger Lakes wine country of upstate New York", Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 1100811 (14 May 2019); https://doi.org/10.1117/12.2518762

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available