Affiliation:
1. School of Agriculture and Environment, Massey University, Palmerston North 4410, New Zealand
2. Massey Agri-Food (MAF) Digital Laboratory, Massey University, Palmerston North 4410, New Zealand
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.
Subject
General Earth and Planetary Sciences
Reference48 articles.
1. (2022). Vineyard Report 2022 New Zealand Winegrowers, New Zealand Winegrowers.
2. Spatial Variability of Grape Composition in a Tempranillo (Vitis vinifera L.) Vineyard over a 3-Year Survey;Baluja;Precis. Agric.,2013
3. Vineyard Variability in Marlborough, New Zealand: Characterising Variation in Vineyard Performance and Options for the Implementation of Precision Viticulture;Bramley;Aust. J. Grape Wine Res.,2011
4. Froment, M., Dampney, P., Goodlass, G., Dawson, C., and Clarke, J. (1995). A Review of Spatial Variation of Nutrients in Soil, Ministry of Agriculture, Fisheries and Food. MAFF final report for project CE0139.
5. Evaluation of the Use of Two-Stage Calibrated PlanetScope Images and Environmental Variables for the Development of the Grapevine Water Status Prediction Model;Wei;Technol. Agron.,2023
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