Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality

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dc.citation.issue5412
dc.citation.volume15
dc.contributor.authorLyu H
dc.contributor.authorGrafton M
dc.contributor.authorRamilan T
dc.contributor.authorIrwin M
dc.contributor.authorWei H-E
dc.contributor.authorSandoval E
dc.contributor.editorZhang C
dc.contributor.editorLiu D
dc.date.accessioned2023-11-19T19:31:49Z
dc.date.accessioned2023-11-20T01:38:08Z
dc.date.available2023-11-20
dc.date.available2023-11-19T19:31:49Z
dc.date.available2023-11-20T01:38:08Z
dc.date.issued2023-11-19
dc.description.abstractThe traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 ◦Brix, and coefficient of determination (R2 ) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 ◦Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 ◦Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.
dc.description.confidentialfalse
dc.identifier.citationLyu H, Grafton M, Ramilan T, Irwin M, Wei H-E, Sandoval E. (2023). Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality. Remote Sensing. 15. 5412.
dc.identifier.elements-typejournal-article
dc.identifier.issn2072-4292
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/69159
dc.languageEnglisg
dc.publisherMDPI AG
dc.relation.isPartOfRemote Sensing
dc.rights(c) 2023 The Author/s
dc.rightsCC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectwine grape
dc.subjectvegetation indices
dc.subjectUAV multispectral imagery
dc.subjectsugar content
dc.titleUsing Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality
dc.typeJournal article
pubs.elements-id484653
pubs.organisational-groupCollege of Sciences
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