Relationship between hyperspectral indices, agronomic parameters and phenolic composition of <i>Vitis vinifera</i> cv Tempranillo grapes

García‐Estévez, Ignacio; Quijada-Morín, Natalia; Rivas-Gonzalo, Julián C.; Martínez-Fernández, José; Sánchez, Nilda; Herrero-Jiménez, Carlos Miguel; Escribano-Bailón, M. Teresa

doi:10.1002/jsfa.8366

Cited by 21 publications

(14 citation statements)

References 47 publications

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“…Specifically, the SVIs that were derived through proximal sensing had the highest correlations with table grape yield characteristics during the middle of veraison, while the satellite-derived SVIs presented their highest correlations during harvest. The latter is in accordance with Sun et al (2017), who found that the best crop stage for estimating wine grape yield from satellite-derived data is before harvest, while Garcia-Estevez et al (2017) found that the highest correlation of NDVI derived from proximal sensing with yield parameters of wine grapes was at veraison [57,58]. Thus, higher resolution data, such as proximal sensing data, can provide earlier crop yield and quality estimations compared to medium resolution remote sensing data.…”

Section: Discussionsupporting

confidence: 87%

Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes

et al. 2018

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Table grapes are a crop with high nutritional value that need to be monitored often to achieve high yield and quality. Non-destructive methods, such as satellite and proximal sensing, are widely used to estimate crop yield and quality characteristics, and spectral vegetation indices (SVIs) are commonly used to present site specific information. The aim of this study was the assessment of SVIs derived from satellite and proximal sensing at different growth stages of table grapes from veraison to harvest. The study took place in a commercial table grape vineyard (Vitis vinifera cv. Thompson Seedless) during three successive cultivation years (2015-2017). The Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were calculated by employing satellite imagery (Landsat 8) and proximal sensing (Crop Circle ACS 470) to assess the yield and quality characteristics of table grapes. The SVIs exhibited different degrees of correlations with different measurement dates and sensing methods. Satellite-based GNDVI at harvest presented higher correlations with crop quality characteristics (r = 0.522 for berry diameter, r = 0.537 for pH, r = 0.629 for berry deformation) compared with NDVI. Proximal-based GNDVI at the middle of veraison presented higher correlations compared with NDVI (r = −0.682 for berry diameter, r = −0.565 for berry deformation). Proximal sensing proved to be more accurate in terms of table grape yield and quality characteristics compared to satellite sensing.

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Section: Discussionsupporting

confidence: 87%

Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes

et al. 2018

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“…Furthermore, the highest correlations were found at the same crop stage for each of the proximal and remote methods, during the middle of veraison (MV-2). This outcome agrees with the findings of Anastasiou et al [29] and Garcia-Estevez et al [92] which found also the highest correlation during the same growth stage (MV-2). Moreover, the highest correlation of S2-values in comparison to the CC-values, during all growth stages and especially in veraison, is probably related to the physiology of the vines.…”

Section: Vis-vbvs and Yield Correlationsupporting

confidence: 93%

Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards

et al. 2021

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Remote-sensing measurements are crucial for smart-farming applications, crop monitoring, and yield forecasting, especially in fields characterized by high heterogeneity. Therefore, in this study, Precision Viticulture (PV) methods using proximal- and remote-sensing technologies were exploited and compared in a table grape vineyard to monitor and evaluate the spatial variation of selected vegetation indices and biophysical variables throughout selected phenological stages (multi-seasonal data), from veraison to harvest. The Normalized Difference Vegetation Index and the Normalized Difference Red-Edge Index were calculated by utilizing satellite imagery (Sentinel-2) and proximal sensing (active crop canopy sensor Crop Circle ACS-470) to assess the correlation between the outputs of the different sensing methods. Moreover, numerous vegetation indices and vegetation biophysical variables (VBVs), such as the Modified Soil Adjusted Vegetation Index, the Normalized Difference Water Index, the Fraction of Vegetation Cover, and the Fraction of Absorbed Photosynthetically Active Radiation, were calculated, using the satellite data. The vegetation indices analysis revealed different degrees of correlation when using diverse sensing methods, various measurement dates, and different parts of the cultivation. The results revealed the usefulness of proximal- and remote-sensing-derived vegetation indices and variables and especially of Normalized Difference Vegetation Index and Fraction of Absorbed Photosynthetically Active Radiation in the monitoring of vineyard condition and yield examining, since they were demonstrated to have a very high degree of correlation (coefficient of determination was 0.87). The adequate correlation of the vegetation indices with the yield during the latter part of the veraison stage provides valuable information for the future estimation of production in broader areas.

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“…Several relevant studies have shown a strong relationship between structural indices calculated from airborne multispectral datasets and fruit quality [17,102]. Compared to traditional structural indices, e.g., NDVI and SR, MTVI has been proven to be sensitive to variations in biophysical parameters by minimizing the asymptotic saturation effect caused by high density of vegetation [92,103]. Similarly, García-Estévez et al [103] found a strong performance of canopy reflectance-based GI for fruit quality estimation in their recent work, and this is confirmed in the current study.…”

Section: Contribution Of Vegetation Indices To Berry Yield and Qualitmentioning

confidence: 99%

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

Maimaitiyiming

Sagan

Kwasniewski³

2019

Remote Sensing

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Reliable assessment of grapevine productivity is a destructive and time-consuming process. In addition, the mixed effects of grapevine water status and scion-rootstock interactions on grapevine productivity are not always linear. Despite the potential opportunity of applying remote sensing and machine learning techniques to predict plant traits, there are still limitations to previously studied techniques for vine productivity due to the complexity of the system not being adequately modeled. During the 2014 and 2015 growing seasons, hyperspectral reflectance spectra were collected using a handheld spectroradiometer in a vineyard designed to investigate the effects of irrigation level (0%, 50%, and 100%) and rootstocks (1103 Paulsen, 3309 Couderc, SO4 and Chambourcin) on vine productivity. To assess vine productivity, it is necessary to measure factors related to fruit ripeness and not just yield, as an over cropped vine may produce high-yield but poor-quality fruit. Therefore, yield, Total Soluble Solids (TSS), Titratable Acidity (TA) and the ratio TSS/TA (maturation index, IMAD) were measured. A total of 20 vegetation indices were calculated from hyperspectral data and used as input for predictive model calibration. Prediction performance of linear/nonlinear multiple regression methods and Weighted Regularized Extreme Learning Machine (WRELM) were compared with our newly developed WRELM-TanhRe. The developed method is based on two activation functions: hyperbolic tangent (Tanh) and rectified linear unit (ReLU). The results revealed that WRELM and WRELM-TanhRe outperformed the widely used multiple regression methods when model performance was tested with an independent validation dataset. WRELM-TanhRe produced the highest prediction accuracy for all the berry yield and quality parameters (R2 of 0.522–0.682 and RMSE of 2–15%), except for TA, which was predicted best with WRELM (R2 of 0.545 and RMSE of 6%). The results demonstrate the value of combining hyperspectral remote sensing and machine learning methods for improving of berry yield and quality prediction.

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Relationship between hyperspectral indices, agronomic parameters and phenolic composition of Vitis vinifera cv Tempranillo grapes

Cited by 21 publications

References 47 publications

Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes

Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes

Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

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