Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation

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dc.citation.issue16
dc.citation.volume13
dc.contributor.authorWei H-E
dc.contributor.authorGrafton M
dc.contributor.authorBretherton M
dc.contributor.authorIrwin M
dc.contributor.authorSandoval E
dc.date.available2021-08
dc.date.issued2021-08-12
dc.description.abstractMonitoring and management of plant water status over the critical period between flower-ing and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R2 = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards.
dc.description.publication-statusPublished
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000690012600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifierARTN 3198
dc.identifier.citationREMOTE SENSING, 2021, 13 (16)
dc.identifier.doi10.3390/rs13163198
dc.identifier.eissn2072-4292
dc.identifier.elements-id448023
dc.identifier.harvestedMassey_Dark
dc.publisherMDPI AG
dc.relation.isPartOfREMOTE SENSING
dc.rightsCopyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
dc.subjecthyperspectral
dc.subjectgrapevine water status
dc.subjectderivative
dc.subjectcontinuum removal
dc.subjectpartial least squares regression
dc.subjectrandom forest regression
dc.subjectsupport vector regression
dc.subjectrecursive feature elimination
dc.subjectensemble
dc.subject.anzsrc0203 Classical Physics
dc.subject.anzsrc0406 Physical Geography and Environmental Geoscience
dc.subject.anzsrc0909 Geomatic Engineering
dc.titleEvaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Agriculture & Environment
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