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

^{1}

^{2}

^{*}

## Abstract

**:**

_{a}) 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 (R

^{2}) 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 R

^{2}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 R

^{2}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.

## 1. Introduction

_{a}and VIs significantly correlated with vine vigor. Furthermore, a portable hyperspectral spectroradiometer can be used to predicted the vine growth status and grape quality within the vineyard scale [14,15].

^{2}values of 0.61. Presently, the increase in computing power and advanced sensing techniques enable more accurate prediction of grape quality, helping grape growers assess grape quality before harvest and thus develop a selective harvesting plan.

^{2}values of 0.65 for grape sugar content using NDVI and automated machine learning [20]. The VIs measured from vine canopy or leaves can reflect the vine vigor, water status, and nutrient status [5,14,21]. Thus, it is important to explore the possibility of using the combination of VIs and machine learning techniques to predict grape quality parameters.

## 2. Methods

#### 2.1. Study Sites

#### 2.2. Grape Sugar Content Data Acquisition

#### 2.3. Canopy and Leaf Reflectance Data Acquisition

#### 2.4. Vine Vigour Parameter Acquisition

#### 2.5. Soil and Terrain Data Acquisition

_{a}) can be used to assess soil texture types and water content [34,35]. Soil EC

_{a}is widely used to explore the spatial variation in soil properties within the vineyards [33]. In addition, one study showed that soil EC

_{a}is directly related to vine water status, berry weight, and sugar content [36]. In this study, an electromagnetic induction sensor EM38-MK2 (Geonics Ltd., Mississauga, ON, Canada) was used to assess the soil EC

_{a}in the study area during 27 May 2021. The EM38-MK2 was operated in the vertical dipole mode, capturing integrated EC

_{a}measurements at a depth of approximately 1.5 m. The EM38-MK2 was towed at the back of an all-terrain vehicle, maintaining a distance of less than 0.2 m between the instrument and the vehicle. To ensure accurate georeferencing of all point data from the EC

_{a}(mS/m) survey, a Trimble Yuma tablet equipped with an onboard GPS receiver (model: Yuma, Trimble) accurate to 2–4 m, was utilized. Soil EC

_{a}points were measured at intervals of approximately 3–10 m along transects, with a 10 m spacing between individual measurements.

#### 2.6. Geostatistical Analysis

_{a}, slope, and elevation value based on ordinary Kriging by using ArcGIS Pro 2.9 (ESRI, Redlands, CA, USA). The soil EC

_{a}values less than 0 mS/m were removed before doing geostatistical analysis. The Kriging interpolation images were then exported to a raster layer with the same gride size as used by the multispectral UAV image. For each sampling vine, the mean values of soil EC

_{a}, elevation, and slope were computed using “zonal statistics as table” in ArcGIS Pro 2.9.

#### 2.7. Machine Learning Model

_{a}, elevation, slope, and day of year for sampling date (DOY). The machine learning models used; included regularized regression, k-Nearest Neighbors (KNN), support vector regression (SVR), random forest regression (RFR), XGBoost, and ANN.

^{2}) and RMSE were selected to compare the performance of different machine learning models on the test set. The Waller–Duncan test was used to conduct multiple comparisons between different machine learning models. The formula of R

^{2}and RMSE was as follows:

## 3. Results

#### 3.1. Variation in Total Soluble Solids

#### 3.2. Pearson’s Correlation Coefficient between VIs and TSS

^{2}of 0.4 and RMSE of 0.89 °Brix (Appendix A). This was followed by RDVI, ARI, EVI, MSAVI, and NGBDI (Appendix A). The OSAVI is calculated based on NIR and red bands; this suggests that OSAVI is a promising candidate to predict the grape TSS when multispectral sensors are available. The promising performance of NGBDI implies that, when only RGB sensors are available, NGBDI may serve as a spectral indicator for grape TSS.

#### 3.3. Spatial Variability of Soil EC_{a}, Elevation and Vine Vigour Status

_{a}, elevation, and vine vigor status within the vineyard. Soil EC

_{a}was measured at 0.5 m depth by an EM38-MK2. The kriging interpolation maps showed the spatial variability of soil EC

_{a}, elevation, and vine vigour status within the vineyard (Figure 6 and Figure 7). In the PN vineyard, soil EC

_{a}values showed lower values in the northeastern portion, higher values in a small portion of the eastern region, and the south-western border (Figure 6a). In the HN vineyard, soil EC

_{a}values were low in the south-western section of the vineyard, and in the north-eastern section showed high soil EC

_{a}values (Figure 7a). When it turns to elevation, elevation values were high in the south-eastern region and low in the north-western region in PN (Figure 6b). In the HN vineyard, elevation values were higher in the middle region and lower in the north-eastern region. The trunk circumference, NDVI and PCD were used to represent the vine vigour status in many studies [21]. Figure 6c and Figure 7c show the spatial variation in TC within study sites. In the PN vineyard, TC values were lower in the south-eastern boundary region and higher in the north-western region (Figure 6c). In HN vineyard, TC values were low in the north-eastern boundary region and high in the northern region (Figure 7c). When it turns to NDVI and PCD, the spatial pattern of these two VIs is similar in each vineyard (Figure 6 and Figure 7). In PN, NDVI and PCD showed high value in the south-western and north-eastern corner, while the low value show in middle region (Figure 6d,e). In HN, NDVI and PCD showed high value in the south-eastern boundary, and low value in the northern region. (Figure 7d,e)

#### 3.4. Prediction Model Performance of Grape TSS

_{a}obtained from proximal sensor, elevation, and slope obtained from a LiDAR camera, as well as TC obtained directly in the vineyards. In order to ensure the robustness and generalization of the models used in this study, the model evaluation process was repeated 20 times with different data splits. When using linear regression models, the best prediction model is ridge regression with an average R

^{2}of 0.31 and RMSE of 1.43 °Brix, respectively (Table 4). For lasso regression, the average result of predicting TSS were a R

^{2}of 0.3 and a RMSE of 1.44 °Brix, respectively (Table 4).

^{2}of 0.52 and RMSE of 1.19 °Brix, respectively (Table 4). The result of using XGBoost showed a similar prediction performance with RFR (R

^{2}= 0.52, RMSE = 1.2 °Brix). RFR and XGBoost aggregated the predictions from multiple decision trees together, to significantly improve the estimation accuracy than the other machine learning models (Figure 8). However, the KNN model showed a significantly poor prediction accuracy compared with other linear and nonlinear regression models with an average R

^{2}of 0.24 and RMSE of 1.2 °Brix (Table 4 and Figure 8). Compared with the linear regression models, SVR significantly improved the prediction performance with an average R

^{2}of 0.37 and RMSE of 1.38 °Brix (Table 4 and Figure 8). However, ANN showed a similar prediction performance as linear regression model with an average R

^{2}of 0.31 and RMSE of 1.43 °Brix (Table 4 and Figure 8).

^{2}of 0.51 and RMSE of 1.19 °Brix, respectively (Table 5). For NGBDI-based model, the RFR model showed the best average result predicting TSS with an R

^{2}of 0.54 and a RMSE of 1.16 °Brix, respectively (Table 6). Figure 9 and Figure 10 showed that the variation of different machine learning models’ performance during the repeated 20 times different data splits strategies. The result of Waller–Duncan showed that using ensemble learning models (RFR and XGBoost) demonstrated greater capability than that of lasso, ridge, KNN, and SVR (Figure 9 and Figure 10). Figure A1 (Appendix A) plots the regression relationship between the best predicted values and actual values for the best performance model (NGBDI-based RFR model).

## 4. Discussion

_{a}, elevation, slope, and TC data as input variables to predict the grape sugar content in a non-destructive way. A total of 236 samples from Pinot Noir cultivars had the TSS measured values based on destructive methods from two commercial vineyards and used as output variable in the regression model. The grape TSS was measured on five different days in the period from veraison to harvest. During the veraison stage, the berries start to mature changing color, softening, accumulating sugar, and reducing acid [37]. From veraison, grape growers start to measure the grape quality parameters such as TSS, pH, and titratable acidity in order to determine the best harvest day. Among them, the TSS is an important parameter to assess the grape maturity as it can determine the alcohol concentration and flavor of the subsequent wine. Figure 4 shows that the sugar content of grapes initially decreased and then increased during the study period. However, in a previous study, the sugar concentration of grapes exhibited a strong increase starting from veraison and eventually reached a plateau during harvest stage [38]. One possible reason is that the sampling vines were randomly selected during each of the five-measurement days, without repeating the selection. This differed from the sampling strategy used in the previous study. Several studies have shown that there is considerable spatial and temporal variation in grape TSS [2,39]. Thus, the grapes in different geographic locations may accumulate sugar at different rates [2]. On each measurement day, the large magnitude of the interquartile range and the outlier in Figure 4 showed the large spatial variability in grape TSS within the vineyard blocks. Thus, it is inappropriate to take a single or average measurements, collected in the vineyard to represent the grape maturity stage.

_{a}value measured from EM38-MK2, elevation and slope value obtained from LiDAR data, and TC measured in the field. The dataset was used to train and test machine learning models, evaluating the performance of linear and nonlinear regression models including lasso regression, ridge regression, KNN, SVR, RFR, XGBoost, and ANN. When we used all input variables in machine learning models, the ensemble method, which included RFR and XGBoost showed similar prediction accuracy for grape TSS prediction, with the best-fitted model achieving R

^{2}= 0.52 and RMSE = 1.19 °Brix. These results confirm the findings of [18], who compared the linear and nonlinear regression models in predicting grape TSS, and they concluded that the AdaBoosting, RFR, and Extra Trees model outperform the other machine learning models [18]. In addition, another study showed that the XGBoost and RFR demonstrated greater capability for modeling crop yield than linear regression model and ANN [48]. Compared with [48], this study tested different machine learning models, 20 times with different test sets, and used Waller–Duncan to analyze the differences between the performance of each model. Furthermore, this study used OSAVI or NGBDI as main input variables with other ancillary data to predict grape TSS based on different machine learning models. We chose OSAVI and NGBDI as the main input variables, as they represent the Vis that can be obtained when different sensors are available. Similar results were obtained with 23 VIs used as input variables, the RFR showed the best prediction performance (Table 4, Table 5 and Table 6). Therefore, the implementation of ensemble learning techniques provide potential to predict the grape TSS in a non-destructive way, based on remote sensed data. However, it should be noted that berry quality was affected by the environmental conditions during the harvest stage (e.g., radiation, temperature). A further study should continue to explore relationships, using different input variables to predict grape TSS. In addition, the berry samples were only 3 berries per vine which cannot represent the whole vine grapes’ TSS value but was used to return a range of TSS values. The sample size may influence the prediction performance when using different machine learning models. Thus, further study should increase the berry sampling numbers.

## 5. Conclusions

_{a}obtained from proximal sensors, elevation, and slope obtained from a LiDAR camera, as well as TC obtained directly in the vineyard, was used to build regression models to estimate grape TSS in a non-destructive way. This study evaluates the prediction performance of seven machine learning techniques: ridge regression, lasso regression, KNN, SVR, RFR, XGBoost, and ANN. The result shows that ensemble learning models (RFR and XGBoost) outperform other regression models when predicting grape TSS. This study develops an alternative approach to predict the grape TSS by different predictor values through various advanced techniques. For grape growers, the approach developed in this study could help them assess the whole vineyard grape TSS in a non-destructive way, in order to make the harvest decision.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

Vis | R^{2} | RMSE | Date |
---|---|---|---|

OSAVI | 0.40 | 0.89 | 14 March |

RDVI | 0.38 | 0.90 | 14 March |

ARI | 0.38 | 0.90 | 14 March |

EVI | 0.38 | 0.90 | 14 March |

MSAVI | 0.38 | 0.90 | 14 March |

NGBDI | 0.35 | 0.92 | 14 March |

NDRE | 0.34 | 0.93 | 14 March |

CLREDEDGE | 0.33 | 0.93 | 14 March |

G% | 0.33 | 0.94 | 14 March |

R_G_index | 0.31 | 0.95 | 14 March |

GNDVI | 0.31 | 0.95 | 14 March |

MCARI | 0.30 | 0.96 | 14 March |

NGRDI | 0.30 | 0.96 | 14 March |

VARI | 0.28 | 0.97 | 14 March |

Clgreen | 0.28 | 0.97 | 14 March |

REGI | 0.27 | 0.97 | 14 March |

MARI | 0.27 | 0.98 | 14 March |

RERI | 0.02 | 1.13 | 14 March |

PCD | 0.01 | 1.14 | 14 March |

MSR | 0.01 | 1.14 | 14 March |

R_B_index | 0.01 | 1.14 | 14 March |

NDVI | 0.01 | 1.14 | 14 March |

OSAVI | 0.40 | 0.89 | 14 March |

**Figure A1.**Measured TSS value comparison against predicted TSS value for the best NGBDI-based RFR model.

## References

- Vineyard Report 2022 New Zealand Winegrowers; New Zealand Winegrowers: Auckland, New Zealand, 2022; pp. 1–22.
- Baluja, J.; Tardaguila, J.; Ayestaran, B.; Diago, M.P. Spatial Variability of Grape Composition in a Tempranillo (Vitis vinifera L.) Vineyard over a 3-Year Survey. Precis. Agric.
**2013**, 14, 40–58. [Google Scholar] [CrossRef] - Bramley, R.G.V.; Trought, M.C.; Praat, J.-P. Vineyard Variability in Marlborough, New Zealand: Characterising Variation in Vineyard Performance and Options for the Implementation of Precision Viticulture. Aust. J. Grape Wine Res.
**2011**, 17, 72–78. [Google Scholar] [CrossRef] - Froment, M.; Dampney, P.; Goodlass, G.; Dawson, C.; Clarke, J. A Review of Spatial Variation of Nutrients in Soil; MAFF final report for project CE0139; Ministry of Agriculture, Fisheries and Food: London, UK, 1995. [Google Scholar]
- Wei, H.-E.; Grafton, M.; Bretherton, M.; Irwin, M.; Sandoval, E. Evaluation of the Use of Two-Stage Calibrated PlanetScope Images and Environmental Variables for the Development of the Grapevine Water Status Prediction Model. Technol. Agron.
**2023**, 3, 6. [Google Scholar] [CrossRef] - Rey-Caramés, C.; Diago, M.P.; Martín, M.P.; Lobo, A.; Tardaguila, J. Using RPAS Multi-Spectral Imagery to Characterise Vigour, Leaf Development, Yield Components and Berry Composition Variability within a Vineyard. Remote Sens.
**2015**, 7, 14458–14481. [Google Scholar] [CrossRef] - Lamb, D.W.; Weedon, M.M.; Bramley, R.G.V. Using Remote Sensing to Predict Grape Phenolics and Colour at Harvest in a Cabernet Sauvignon Vineyard: Timing Observations against Vine Phenology and Optimising Image Resolution. Aust. J. Grape Wine Res.
**2004**, 10, 46–54. [Google Scholar] [CrossRef] - Arab, S.T.; Ahamed, T. Land Suitability Analysis for Potential Vineyards Extension in Afghanistan at Regional Scale Using Remote Sensing Datasets. Remote Sens.
**2022**, 14, 4450. [Google Scholar] [CrossRef] - Arab, S.T.; Noguchi, R.; Matsushita, S.; Ahamed, T. Prediction of Grape Yields from Time-Series Vegetation Indices Using Satellite Remote Sensing and a Machine-Learning Approach. Remote Sens. Appl. Soc. Environ.
**2021**, 22, 100485. [Google Scholar] [CrossRef] - Carrillo, E.; Matese, A.; Rousseau, J.; Tisseyre, B. Use of Multi-Spectral Airborne Imagery to Improve Yield Sampling in Viticulture. Precis. Agric.
**2016**, 17, 74–92. [Google Scholar] [CrossRef] - Matese, A.; Toscano, P.; Di Gennaro, S.F.; Genesio, L.; Vaccari, F.P.; Primicerio, J.; Belli, C.; Zaldei, A.; Bianconi, R.; Gioli, B. Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture. Remote Sens.
**2015**, 7, 2971–2990. [Google Scholar] [CrossRef] - García-Fernández, M.; Sanz-Ablanedo, E.; Rodríguez-Pérez, J.R. High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability. Agronomy
**2021**, 11, 655. [Google Scholar] [CrossRef] - Wei, H.-E.; Grafton, M.; Bretherton, M.; Irwin, M.; Sandoval, E. Evaluation of the Use of UAV-Derived Vegetation Indices and Environmental Variables for Grapevine Water Status Monitoring Based on Machine Learning Algorithms and SHAP Analysis. Remote Sens.
**2022**, 14, 5918. [Google Scholar] [CrossRef] - Lyu, H.; Grafton, M.; Ramilan, T.; Irwin, M.; Sandoval, E. Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models. Remote Sens.
**2023**, 15, 1497. [Google Scholar] [CrossRef] - Kalopesa, E.; Karyotis, K.; Tziolas, N.; Tsakiridis, N.; Samarinas, N.; Zalidis, G. Estimation of Sugar Content in Wine Grapes via In Situ VNIR–SWIR Point Spectroscopy Using Explainable Artificial Intelligence Techniques. Sensors
**2023**, 23, 1065. [Google Scholar] [CrossRef] [PubMed] - Bramley, R.; Pearse, B.; Chamberlain, P. Being Profitable Precisely-A Case Study of Precision Viticulture from Margaret River. Aust. New Zealand Grapegrow. Winemak. [Annu. Tech. Issue]
**2003**, 473a, 84–87. [Google Scholar] - Benelli, A.; Cevoli, C.; Ragni, L.; Fabbri, A. In-Field and Non-Destructive Monitoring of Grapes Maturity by Hyperspectral Imaging. Biosyst. Eng.
**2021**, 207, 59–67. [Google Scholar] [CrossRef] - Kasimati, A.; Espejo-Garcia, B.; Vali, E.; Malounas, I.; Fountas, S. Investigating a Selection of Methods for the Prediction of Total Soluble Solids among Wine Grape Quality Characteristics Using Normalized Difference Vegetation Index Data from Proximal and Remote Sensing. Front. Plant Sci.
**2021**, 12, 683078. [Google Scholar] [CrossRef] [PubMed] - Dambergs, R.; Gishen, M.; Cozzolino, D. A Review of the State of the Art, Limitations, and Perspectives of Infrared Spectroscopy for the Analysis of Wine Grapes, Must, and Grapevine Tissue. Appl. Spectrosc. Rev.
**2015**, 50, 261–278. [Google Scholar] [CrossRef] - Kasimati, A.; Espejo-García, B.; Darra, N.; Fountas, S. Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and Automated Machine Learning. Sensors
**2022**, 22, 3249. [Google Scholar] [CrossRef] - Bramley, R.G.V. Precision Viticulture: Managing Vineyard Variability for Improved Quality Outcomes. In Managing Wine Quality; Elsevier: Amsterdam, The Netherlands, 2022; pp. 541–586. [Google Scholar]
- Arnó Satorra, J.; Martínez Casasnovas, J.A.; Ribes Dasi, M.; Rosell Polo, J.R. Precision Viticulture. Research Topics, Challenges and Opportunities in Site-Specific Vineyard Management. Span. J. Agric. Res.
**2009**, 7, 779–790. [Google Scholar] [CrossRef] - Rouse, J.W., Jr.; Haas, R.H.; Deering, D.W.; Schell, J.A.; Harlan, J.C. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation; National Aeronautics and Space Administration: Washington, DC, USA, 1974; pp. 1–120. [Google Scholar]
- Romero, M.; Luo, Y.; Su, B.; Fuentes, S. Vineyard Water Status Estimation Using Multispectral Imagery from an UAV Platform and Machine Learning Algorithms for Irrigation Scheduling Management. Comput. Electron. Agric.
**2018**, 147, 109–117. [Google Scholar] [CrossRef] - Gil-Pérez, B.; Zarco-Tejada, P.J.; Correa-Guimaraes, A.; Relea-Gangas, E.; Navas-Gracia, L.M.; Hernández-Navarro, S.; Sanz-Requena, J.F.; Berjón, A.; Martín-Gil, J. Remote Sensing Detection of Nutrient Uptake in Vineyards Using Narrow-Band Hyperspectral Imagery. Vitis
**2010**, 49, 167–173. [Google Scholar] - Jiménez-Brenes, F.M.; Lopez-Granados, F.; Torres-Sánchez, J.; Peña, J.M.; Ramírez, P.; Castillejo-González, I.L.; de Castro, A.I. Automatic UAV-Based Detection of Cynodon Dactylon for Site-Specific Vineyard Management. PLoS ONE
**2019**, 14, e0218132. [Google Scholar] [CrossRef] [PubMed] - Brook, A.; De Micco, V.; Battipaglia, G.; Erbaggio, A.; Ludeno, G.; Catapano, I.; Bonfante, A. A Smart Multiple Spatial and Temporal Resolution System to Support Precision Agriculture from Satellite Images: Proof of Concept on Aglianico Vineyard. Remote Sens. Environ.
**2020**, 240, 111679. [Google Scholar] [CrossRef] - Albetis, J.; Duthoit, S.; Guttler, F.; Jacquin, A.; Goulard, M.; Poilvé, H.; Féret, J.-B.; Dedieu, G. Detection of Flavescence Dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery. Remote Sens.
**2017**, 9, 308. [Google Scholar] [CrossRef] - Soubry, I.; Patias, P.; Tsioukas, V. Monitoring Vineyards with UAV and Multi-Sensors for the Assessment of Water Stress and Grape Maturity. J. Unmanned Veh. Syst.
**2017**, 5, 37–50. [Google Scholar] [CrossRef] - Cogato, A.; Pagay, V.; Marinello, F.; Meggio, F.; Grace, P.; De Antoni Migliorati, M. Assessing the Feasibility of Using Sentinel-2 Imagery to Quantify the Impact of Heatwaves on Irrigated Vineyards. Remote Sens.
**2019**, 11, 2869. [Google Scholar] [CrossRef] - Pádua, L.; Marques, P.; Hruška, J.; Adão, T.; Bessa, J.; Sousa, A.; Peres, E.; Morais, R.; Sousa, J.J. Vineyard Properties Extraction Combining UAS-Based RGB Imagery with Elevation Data. Int. J. Remote Sens.
**2018**, 39, 5377–5401. [Google Scholar] [CrossRef] - Albetis, J.; Jacquin, A.; Goulard, M.; Poilvé, H.; Rousseau, J.; Clenet, H.; Dedieu, G.; Duthoit, S. On the Potentiality of UAV Multispectral Imagery to Detect Flavescence Dorée and Grapevine Trunk Diseases. Remote Sens.
**2018**, 11, 23. [Google Scholar] [CrossRef] - Bramley, R.G.V.; Ouzman, J.; Boss, P.K. Variation in Vine Vigour, Grape Yield and Vineyard Soils and Topography as Indicators of Variation in the Chemical Composition of Grapes, Wine and Wine Sensory Attributes. Aust. J. Grape Wine Res.
**2011**, 17, 217–229. [Google Scholar] [CrossRef] - SU, S.L.; Singh, D.N.; Baghini, M.S. A Critical Review of Soil Moisture Measurement. Measurement
**2014**, 54, 92–105. [Google Scholar] [CrossRef] - Trought, M.C.; Dixon, R.; Mills, T.; Greven, M.; Agnew, R.; Mauk, J.L.; Praat, J.-P. The Impact of Differences in Soil Texture within a Vineyard on Vine Vigour, Vine Earliness and Juice Composition. OENO One
**2008**, 42, 67–72. [Google Scholar] [CrossRef] - Yu, R.; Brillante, L.; Martínez-Lüscher, J.; Kurtural, S.K. Spatial Variability of Soil and Plant Water Status and Their Cascading Effects on Grapevine Physiology Are Linked to Berry and Wine Chemistry. Front. Plant Sci.
**2020**, 11, 790. [Google Scholar] [CrossRef] [PubMed] - Keller, M. The Science of Grapevines; Academic Press: Cambridge, MA, USA, 2020; ISBN 0-12-816702-5. [Google Scholar]
- Trought, M.C.; Bramley, R.G. Vineyard Variability in Marlborough, New Zealand: Characterising Spatial and Temporal Changes in Fruit Composition and Juice Quality in the Vineyard. Aust. J. Grape Wine Res.
**2011**, 17, 79–89. [Google Scholar] [CrossRef] - Bramley, R.G.V. Understanding Variability in Winegrape Production Systems 2. Within Vineyard Variation in Quality over Several Vintages. Aust. J. Grape Wine Res.
**2005**, 11, 33–42. [Google Scholar] [CrossRef] - Gomes, V.M.; Fernandes, A.M.; Faia, A.; Melo-Pinto, P. Comparison of Different Approaches for the Prediction of Sugar Content in New Vintages of Whole Port Wine Grape Berries Using Hyperspectral Imaging. Comput. Electron. Agric.
**2017**, 140, 244–254. [Google Scholar] [CrossRef] - Sanches, I.D.; Souza Filho, C.R.; Kokaly, R.F. Spectroscopic Remote Sensing of Plant Stress at Leaf and Canopy Levels Using the Chlorophyll 680 Nm Absorption Feature with Continuum Removal. ISPRS J. Photogramm. Remote Sens.
**2014**, 97, 111–122. [Google Scholar] [CrossRef] - Giovos, R.; Tassopoulos, D.; Kalivas, D.; Lougkos, N.; Priovolou, A. Remote Sensing Vegetation Indices in Viticulture: A Critical Review. Agriculture
**2021**, 11, 457. [Google Scholar] [CrossRef] - Bramley, R.G.V.; Ouzman, J.; Trought, M.C.; Neal, S.M.; Bennett, J.S. Spatio-temporal Variability in Vine Vigour and Yield in a Marlborough Sauvignon Blanc Vineyard. Aust. J. Grape Wine Res.
**2019**, 25, 430–438. [Google Scholar] [CrossRef] - Proffitt, T.; Malcolm, A. Implementing Zonal Vineyard Management through Airborne Remote Sensing. Aust. New Zealand Grapegrow. Winemak.
**2005**, 22–31. [Google Scholar] - Hall, A.; Louis, J.; Lamb, D. Characterising and Mapping Vineyard Canopy Using High-Spatial-Resolution Aerial Multispectral Images. Comput. Geosci.
**2003**, 29, 813–822. [Google Scholar] [CrossRef] - Anastasiou, E.; Balafoutis, A.; Darra, N.; Psiroukis, V.; Biniari, A.; Xanthopoulos, G.; Fountas, S. Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes. Agriculture
**2018**, 8, 94. [Google Scholar] [CrossRef] - Sun, L.; Gao, F.; Anderson, M.C.; Kustas, W.P.; Alsina, M.M.; Sanchez, L.; Sams, B.; McKee, L.; Dulaney, W.; White, W.A. Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards. Remote Sens.
**2017**, 9, 317. [Google Scholar] [CrossRef] - Jiang, G.; Grafton, M.C.; Pearson, D.; Bretherton, M.R.; Holmes, A. A Comparison of Supervised Machine Learning Algorithms for Predicting Subfield Yield Variability of Maize Grain. J. ASABE
**2022**, 65, 287–294. [Google Scholar] [CrossRef]

**Figure 1.**The location of sampling vines in PN (

**a**); HN (

**b**). (Points represent the location of sampling vines).

**Figure 2.**Total daily precipitation, average temperature, and irradiance recorded by on-site weather station (blue bar represents the precipitation).

**Figure 5.**Pearson’s correlation coefficient between VIs and grape TSS (Different colors represent different sampling date).

**Figure 6.**Spatial interpolation map of soil EC

_{a}(

**a**); elevation (

**b**); trunk circumference (

**c**); NDVI (

**d**); PCD (

**e**) in PN vineyard.

**Figure 7.**Spatial interpolation map of soil EC

_{a}(

**a**); elevation (

**b**); trunk circumference (

**c**); NDVI (

**d**); PCD (

**e**) in HN vineyard.

**Figure 8.**The boxplot of different machine learning model performance in R

^{2}(

**a**); RMSE (

**b**). (Different letters between any two groups represents significant difference between them, if two groups have the same letter then this indicates that they are not statistically different).

**Figure 9.**The boxplot of different OSAVI-based model performance in R

^{2}(

**a**); RMSE (

**b**). (Different letters between any two groups represents significant difference between them, if two groups have the same letter then this indicates that they are not statistically different).

**Figure 10.**The boxplot of different NGBDI-based model performance in R

^{2}(

**a**); RMSE (

**b**). (Different letters between any two groups represents significant difference between them, if two groups have the same letter then this indicates that they are not statistically different).

Vine Phenology | Date |
---|---|

Budburst | September, October |

Leaf development | October, November |

Inflorescence emergence | November |

Flowering | November, Early December |

Fruit set and fruiting | December, January |

Veraison | Late January, February |

Harvest | March, April |

Vineyard | 20 February | 26 February | 2 March | 6 March | 14 March |
---|---|---|---|---|---|

Pencarrow | 6 | 25 | 37 | 32 | 18 |

Hua Nui | 25 | 36 | 14 | 30 | 13 |

Vegetation Indices | Formula | Reference |
---|---|---|

PCD | $\mathrm{N}\mathrm{I}\mathrm{R}/\mathrm{R}\mathrm{e}\mathrm{d}$ | [22] |

NDVI | $(\mathrm{N}\mathrm{I}\mathrm{R}-\mathrm{R}\mathrm{e}\mathrm{d})/(\mathrm{N}\mathrm{I}\mathrm{R}+\mathrm{R}\mathrm{e}\mathrm{d})$ | [23] |

GNDVI | $(\mathrm{N}\mathrm{I}\mathrm{R}-\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n})/(\mathrm{N}\mathrm{I}\mathrm{R}+\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n})$ | [24] |

MSR | $(\left(\frac{\mathrm{N}\mathrm{I}\mathrm{R}}{\mathrm{R}\mathrm{e}\mathrm{d}}\right)-1)/\sqrt{(\left(\frac{\mathrm{N}\mathrm{I}\mathrm{R}}{\mathrm{R}\mathrm{e}\mathrm{d}}\right)+1)}$ | [25] |

MSAVI | $\frac{2\times \mathrm{N}\mathrm{I}\mathrm{R}+1-\sqrt{{\left(2\times \mathrm{N}\mathrm{I}\mathrm{R}+1\right)}^{2}-8\times \left(\mathrm{N}\mathrm{I}\mathrm{R}-\mathrm{R}\mathrm{e}\mathrm{d}\right)}}{2}$ | [25] |

RDVI | $\left(\mathrm{N}\mathrm{I}\mathrm{R}-\mathrm{R}\mathrm{e}\mathrm{d}\right)/\sqrt{(\mathrm{N}\mathrm{I}\mathrm{R}+\mathrm{R}\mathrm{e}\mathrm{d})}$ | [25] |

OSAVI | $\left(1+0.16\right)\frac{\mathrm{N}\mathrm{I}\mathrm{R}-\mathrm{R}\mathrm{e}\mathrm{d}}{\mathrm{N}\mathrm{I}\mathrm{R}+\mathrm{R}\mathrm{e}\mathrm{d}+0.16}$ | [25] |

R/B index | $\mathrm{R}\mathrm{e}\mathrm{d}/\mathrm{B}\mathrm{l}\mathrm{u}\mathrm{e}$ | [26] |

R/G index | $\mathrm{R}\mathrm{e}\mathrm{d}/\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$ | [26] |

NGRDI | $(\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}-\mathrm{R}\mathrm{e}\mathrm{d})/(\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}+\mathrm{R}\mathrm{e}\mathrm{d})$ | [26] |

NPCI | $(\mathrm{B}\mathrm{l}\mathrm{u}\mathrm{e}-\mathrm{R}\mathrm{e}\mathrm{d})/(\mathrm{B}\mathrm{l}\mathrm{u}\mathrm{e}+\mathrm{R}\mathrm{e}\mathrm{d})$ | [26] |

VARI | $(\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}-\mathrm{R}\mathrm{e}\mathrm{d})/(\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}+\mathrm{R}\mathrm{e}\mathrm{d}-\mathrm{B}\mathrm{l}\mathrm{u}\mathrm{e})$ | [27] |

Clgreen | $\mathrm{N}\mathrm{I}\mathrm{R}/\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}-1$ | [26] |

ARI | ${\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}^{-1}-{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}}^{-1}$ | [28] |

MARI | $\left({\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}^{-1}-{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}}^{-1}\right)\times \mathrm{N}\mathrm{I}\mathrm{R}$ | [28] |

CLREDEDGE | $\left(\mathrm{N}\mathrm{I}\mathrm{R}/\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}\right)-1$ | [29] |

MCARI | $\left(\left(\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}-\mathrm{R}\mathrm{e}\mathrm{d}\right)-0.2\times \left(\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}-\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}\right)\right)\times (\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}/\mathrm{R}\mathrm{e}\mathrm{d})$ | [29] |

NDRE | $(\mathrm{N}\mathrm{I}\mathrm{R}-\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e})/(\mathrm{N}\mathrm{I}\mathrm{R}+\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e})$ | [29] |

EVI | $2.5\times (\mathrm{N}\mathrm{I}\mathrm{R}-\mathrm{R}\mathrm{e}\mathrm{d})/(\mathrm{N}\mathrm{I}\mathrm{R}+6\times \mathrm{R}\mathrm{e}\mathrm{d}-7.5\times \mathrm{B}\mathrm{l}\mathrm{u}\mathrm{e}+1)$ | [30] |

NGBDI | $(\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}-\mathrm{B}\mathrm{l}\mathrm{u}\mathrm{e})/(\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}+\mathrm{B}\mathrm{l}\mathrm{u}\mathrm{e})$ | [31] |

G% | $\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}/(\mathrm{R}\mathrm{e}\mathrm{d}+\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}+\mathrm{B}\mathrm{l}\mathrm{u}\mathrm{e})$ | [31] |

REGI | $(\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}-\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n})/(\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}+\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n})$ | [32] |

RERI | $(\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}-\mathrm{R}\mathrm{e}\mathrm{d})/(\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}+\mathrm{R}\mathrm{e}\mathrm{d})$ | [32] |

**Table 4.**The prediction performance of study machine learning models (bold represents the best prediction performance).

Method | R^{2} | RMSE |
---|---|---|

Lasso regression | 0.3 ± 0.08 | 1.44 ± 0.1 |

Ridge regression | 0.31 ± 0.08 | 1.43 ± 0.1 |

KNN | 0.24 ± 0.08 | 1.5 ± 0.1 |

SVR | 0.37 ± 0.08 | 1.38 ± 0.09 |

RFR | 0.52 ± 0.06 | 1.19 ± 0.07 |

XGBoost | 0.52 ± 0.06 | 1.2 ± 0.09 |

ANN | 0.31 ± 0.11 | 1.43 ± 0.13 |

**Table 5.**The predicted performance of OSAVI-based models (bold represents the best prediction performance).

Method | R^{2} | RMSE |
---|---|---|

Lasso regression | 0.3 ± 0.08 | 1.44 ± 0.1 |

Ridge regression | 0.32 ± 0.1 | 1.38 ± 0.1 |

KNN | 0.4 ± 0.07 | 1.3 ± 0.1 |

SVR | 0.36 ± 0.07 | 1.39 ± 0.1 |

RFR | 0.51 ± 0.07 | 1.19 ± 0.07 |

XGBoost | 0.5 ± 0.07 | 1.21 ± 0.08 |

ANN | 0.45 ± 0.12 | 1.26 ± 0.14 |

**Table 6.**The predicted performance of NGBDI-based models (bold represents the best prediction performance).

Method | R^{2} | RMSE |
---|---|---|

Lasso regression | 0.3 ± 0.08 | 1.43 ± 0.09 |

Ridge regression | 0.32 ± 0.1 | 1.43 ± 0.09 |

KNN | 0.4 ± 0.06 | 1.31 ± 0.1 |

SVR | 0.4 ± 0.07 | 1.33 ± 0.12 |

RFR | 0.54 ± 0.07 | 1.16 ± 0.07 |

XGBoost | 0.52 ± 0.06 | 1.19 ± 0.07 |

ANN | 0.39 ± 0.11 | 1.31 ± 0.11 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 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 (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Lyu, H.; Grafton, M.; Ramilan, T.; Irwin, M.; Wei, H.-E.; Sandoval, E.
Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality. *Remote Sens.* **2023**, *15*, 5412.
https://doi.org/10.3390/rs15225412

**AMA Style**

Lyu H, Grafton M, Ramilan T, Irwin M, Wei H-E, Sandoval E.
Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality. *Remote Sensing*. 2023; 15(22):5412.
https://doi.org/10.3390/rs15225412

**Chicago/Turabian Style**

Lyu, Hongyi, Miles Grafton, Thiagarajah Ramilan, Matthew Irwin, Hsiang-En Wei, and Eduardo Sandoval.
2023. "Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality" *Remote Sensing* 15, no. 22: 5412.
https://doi.org/10.3390/rs15225412