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Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning

Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery

Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA
Horticulture Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
Cornell Lake Erie Research and Extension Laboratory, Cornell AgriTech, Portland, NY 14769, USA
Author to whom correspondence should be addressed.
Academic Editors: Giovanni Avola and Alessandro Matese
Remote Sens. 2021, 13(21), 4489;
Received: 24 September 2021 / Revised: 31 October 2021 / Accepted: 5 November 2021 / Published: 8 November 2021
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400–2500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutrient values, to select the optimal bands for the nutrient regression models. The canopy spectral data were obtained with unmanned aerial systems (UAS), using push-broom imaging spectrometers (hyperspectral sensors). The novel use of UAS-based hyperspectral imagery to assess the grapevine nutrient status fills the gap between in situ spectral sampling and UAS-based multispectral imaging, avoiding their inherent trade-offs between spatial and spectral resolution. We found that an ensemble feature ranking method, utilizing six different machine learning feature selection methods, produced similar regression results as the standard PLSR feature selection and regression while generally selecting fewer wavelengths. We identified a set of biochemically consistent bands (606, 641, and 1494 nm) to predict the nitrogen content with an RMSE of 0.17% (using leave-one-out cross-validation) in samples with nitrogen contents ranging between 2.4 and 3.6%. Further studying is needed to confirm the relevance and consistency of the wavelengths selected for each nutrient model, but ensemble feature selection showed promise in identifying stable sets of wavelengths for assessing grapevine nutrient contents from canopy spectra. View Full-Text
Keywords: imaging spectroscopy; unmanned aerial systems; vineyard; nutrients imaging spectroscopy; unmanned aerial systems; vineyard; nutrients
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MDPI and ACS Style

Chancia, R.; Bates, T.; Vanden Heuvel, J.; van Aardt, J. Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery. Remote Sens. 2021, 13, 4489.

AMA Style

Chancia R, Bates T, Vanden Heuvel J, van Aardt J. Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery. Remote Sensing. 2021; 13(21):4489.

Chicago/Turabian Style

Chancia, Robert, Terry Bates, Justine Vanden Heuvel, and Jan van Aardt. 2021. "Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery" Remote Sensing 13, no. 21: 4489.

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