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

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 22; p. 5412
• Main Authors: Lyu, Hongyi, Grafton, Miles, Ramilan, Thiagarajah, Irwin, Matthew, Wei, Hsiang-En, Sandoval, Eduardo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2023
• Subjects: Aircraft; Artificial neural networks; Berries; Brix value; Cameras; Correlation coefficient; Correlation coefficients; Electrical conductivity; Electrical resistivity; Fruits; Grapes; Learning algorithms; Machine learning; Neural networks; Normalized difference vegetative index; Prediction models; Quality management; Regression analysis; Regression models; Remote sensing; Root-mean-square errors; Satellites; Sensors; Soil conductivity; Soils; sugar content; Support vector machines; UAV multispectral imagery; Unmanned aerial vehicles; Vegetation; vegetation indices; Vineyards; wine grape; Wine industry; Wineries; Wineries & vineyards; Wines; New Zealand
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15225412

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.