Utilization of remote sensing technology to detect riesling vineyard variability
ineyard blocks can vary spatially with respect to several viticulturally significant qualities such as soil variables, vine vigor, vine physiology
Aroma and quality assessment for vertical vintages using machine learning modelling based on weather and management information
Wine quality traits are usually given by parameters such as aroma profile, total acidity, alcohol content, colour and phenolic content, among others
Hyperspectral imaging and cnn for on‐the‐go, non‐destructive assessment of grape composition in the vineyard
Knowledge of the spatial‐temporal variation of the grape composition within a vineyard may assist decision making regarding sampling
Sensitivity of vis‐nir spectral indices to detect nitrogen deficiency and canopy function in cv. Barbera (Vitis vinifera L.) Grapevines
Precision nutrient management in viticulture can be addressed on the basis of a spatial characterization of within‐vineyard vine
Organic and biodynamic viticulture affect soil quality and soil microbial diversity
The production of organically grown crops developed exponentially in the last few decades based on consumer demands for healthy food