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Article

Predicting Water Stress in Wild Blueberry Fields Using Airborne Visible and Near Infrared Imaging Spectroscopy

1
School of Forest Resources, University of Maine, Orono, ME 04469, USA
2
Schoodic Institute, 9 Atterbury Circle, P.O. Box 277, Winter Harbor, ME 04693, USA
3
School of Biology and Ecology, University of Maine, Orono, ME 04469, USA
4
Jasper Wyman & Son, P.O. Box 100, Milbridge, ME 04658, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Jaime Zabalza
Remote Sens. 2021, 13(8), 1425; https://doi.org/10.3390/rs13081425
Received: 28 February 2021 / Revised: 30 March 2021 / Accepted: 3 April 2021 / Published: 7 April 2021
(This article belongs to the Special Issue Feature Extraction and Data Classification in Hyperspectral Imaging)
Water management and irrigation practices are persistent challenges for many agricultural systems, exacerbated by changing seasonal and weather patterns. The wild blueberry industry is at heightened susceptibility due to its unique growing conditions and uncultivated nature. Stress detection in agricultural fields can prompt management responses to mitigate detrimental conditions, including drought and disease. We assessed airborne spectral data accompanied by ground sampled water potential over three developmental stages of wild blueberries collected throughout the 2019 summer on two adjacent fields, one irrigated and one non-irrigated. Ground sampled leaves were collected in tandem to the hyperspectral image collection with an unoccupied aerial vehicle (UAV) and then measured for leaf water potential. Using methods in machine learning and statistical analysis, we developed models to determine irrigation status and water potential. Seven models were assessed in this study, with four used to process six hyperspectral cube images for analysis. These images were classified as irrigated or non-irrigated and estimated for water potential levels, resulting in an R2 of 0.62 and verified with a validation dataset. Further investigation relating imaging spectroscopy and water potential will be beneficial in understanding the dynamics between the two for future studies. View Full-Text
Keywords: hyperspectral; agriculture; vegetation indices; irrigation; machine learning; water potential; UAV; VNIR; reflectance hyperspectral; agriculture; vegetation indices; irrigation; machine learning; water potential; UAV; VNIR; reflectance
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MDPI and ACS Style

Chan, C.; Nelson, P.R.; Hayes, D.J.; Zhang, Y.-J.; Hall, B. Predicting Water Stress in Wild Blueberry Fields Using Airborne Visible and Near Infrared Imaging Spectroscopy. Remote Sens. 2021, 13, 1425. https://doi.org/10.3390/rs13081425

AMA Style

Chan C, Nelson PR, Hayes DJ, Zhang Y-J, Hall B. Predicting Water Stress in Wild Blueberry Fields Using Airborne Visible and Near Infrared Imaging Spectroscopy. Remote Sensing. 2021; 13(8):1425. https://doi.org/10.3390/rs13081425

Chicago/Turabian Style

Chan, Catherine, Peter R. Nelson, Daniel J. Hayes, Yong-Jiang Zhang, and Bruce Hall. 2021. "Predicting Water Stress in Wild Blueberry Fields Using Airborne Visible and Near Infrared Imaging Spectroscopy" Remote Sensing 13, no. 8: 1425. https://doi.org/10.3390/rs13081425

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