Precision viticulture methods should be adapted to the viticultural context to provide relevant insights. This study evaluated the potential and constraints of established precision viticulture techniques in the Valpolicella wine region, which is characterised by small, irregularly shaped vineyard parcels trained using the traditional overhead Pergola system, and where mixed plots of autochthonous cultivars are often grown. Two vineyards were monitored for intra-vineyard variability, one of which was assessed over two seasons. Sentinel-2 and UAV-derived parameters were measured along with ground-based sensor data, including the canopy crop water stress index (CWSI), soil volumetric water content (VWC), and NDVI. Traditional agronomic measurements and berry quality parameters were also collected and integrated with sensor data into a dataset, which was then explored using multivariate analysis to validate the sensor data. Despite structural features that were expected to challenge top-down remote sensing, the Sentinel-2 and UAV-derived data exhibited highly similar spatial patterns and were in turn consistent with ground-based measurements. This result may be attributable to the horizontal pergola canopy, which enhanced its visibility from above. In the autochthonous multivarietal vineyard, the sensor-derived data successfully identified intra-parcel variability in agronomic parameters, independently of the cultivar plot layout. Skin thickness, a highly relevant berry trait for Valpolicella wines produced from withered grapes, was negatively correlated with CWSI. This study highlights the value of an integrated multiple-parameter approach that leverages sensor-derived data and paves the way for precision viticulture application in the Valpolicella.
Shmuleviz et al. (Fri,) studied this question.