High-resolution aerial thermography for water stress estimation in grapevines

Aims: The aims of this study were to (1) detect alterations in the reflectance spectra of vines with fungal diseases, (2) map these alterations, and (3) determine the best wavelengths which may be used as early indicators of fungal diseases in vines.

One of the most concerning trends associated with increasing heat and water stress is advanced ripening of grapes, which leads to harvesting fruit at higher sugar concentrations but lacking optimal phenolic (i.e. color and mouthfeel) and aromatic maturity. Mitigation techniques for this phenomenon have been studied for many years and practices to delay sugar accumulation have been identified, including antitranspirants, delayed pruning and late-source-limitation techniques. Evaluation of the efficacy of these vineyard practices has occurred across a wide range of environments, vintages, varieties and growing conditions. To assess the broader efficacy of these three vineyard practices, which are easy-to-implement and cost-effective, a meta-analytic approach was adopted using data retrieved from 43 original studies.

In California vineyards, irrigation is considered as one of the most important decisions growers will make. Recent research has revealed that decisions of when to begin irrigation and how much water to apply have considerable consequences for final grape quality and hence wine quality. However, it is unclear whether and to what extent the average winegrape grower uses objective data to begin irrigating or to determine the amount of water to apply.

The objective of this investigation was to verify usefulness of proximal sensing technology and unmanned aerial vehicles (UAVs) for mapping variables e.g., vine size (potential vigor), soil and vine water status, yield, fruit composition, and virus incidence in vineyards.

The need to rationalize agricultural inputs has recently increased interest in assessing vineyard variability in order to implement variable rate input applications, so-called ‘precision viticulture’. In many viticultural areas globally, precision viticulture is already widely used such as for selective harvesting and variable rate application (VRA) of inputs such as irrigation and/or fertilizer. Robust VRA relies on having a geostatistically accurate map (of one or more vineyard attributes) requiring high sampling densities, which can be cost- and time-prohibitive to obtain. Previous work on spatial interpolation using kriging have upscaled ground-based measurements, but such upscaling strategies are applicable only when vineyard conditions are spatially continuous and satisfies the assumption of second-order stationary processes. Alternatively, mixed models that combine kriging and auxiliary information, such as the regression kriging (RK) method, are more instructive for spatial predictions. In order to improve prediction accuracies, it is therefore necessary to incorporate additional information to achieve accurate spatial patterns with low error.