Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality
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Date
2023-11-19
DOI
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI AG
Rights
(c) 2023 The Author/s
CC BY
CC BY
Abstract
The 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.
Description
Keywords
wine grape, vegetation indices, UAV multispectral imagery, sugar content
Citation
Lyu 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.