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

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 nut...

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Autores principales:	Robert Chancia, Terry Bates, Justine Vanden Heuvel, Jan van Aardt
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	imaging spectroscopy unmanned aerial systems vineyard nutrients Science Q
Acceso en línea:	https://doaj.org/article/3bd85f3906e44fe091e652ff8c666833
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spelling	oai:doaj.org-article:3bd85f3906e44fe091e652ff8c6668332021-11-11T18:59:25ZAssessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery10.3390/rs132144892072-4292https://doaj.org/article/3bd85f3906e44fe091e652ff8c6668332021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4489https://doaj.org/toc/2072-4292This 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.Robert ChanciaTerry BatesJustine Vanden HeuvelJan van AardtMDPI AGarticleimaging spectroscopyunmanned aerial systemsvineyardnutrientsScienceQENRemote Sensing, Vol 13, Iss 4489, p 4489 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	imaging spectroscopy unmanned aerial systems vineyard nutrients Science Q
spellingShingle	imaging spectroscopy unmanned aerial systems vineyard nutrients Science Q Robert Chancia Terry Bates Justine Vanden Heuvel Jan van Aardt Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery
description	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.
format	article
author	Robert Chancia Terry Bates Justine Vanden Heuvel Jan van Aardt
author_facet	Robert Chancia Terry Bates Justine Vanden Heuvel Jan van Aardt
author_sort	Robert Chancia
title	Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery
title_short	Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery
title_full	Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery
title_fullStr	Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery
title_full_unstemmed	Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery
title_sort	assessing grapevine nutrient status from unmanned aerial system (uas) hyperspectral imagery
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3bd85f3906e44fe091e652ff8c666833
work_keys_str_mv	AT robertchancia assessinggrapevinenutrientstatusfromunmannedaerialsystemuashyperspectralimagery AT terrybates assessinggrapevinenutrientstatusfromunmannedaerialsystemuashyperspectralimagery AT justinevandenheuvel assessinggrapevinenutrientstatusfromunmannedaerialsystemuashyperspectralimagery AT janvanaardt assessinggrapevinenutrientstatusfromunmannedaerialsystemuashyperspectralimagery
_version_	1718431637819097088

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

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