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The determination of time for grape harvest is probably the most important decision for wine making producers, because grapes are not climacteric fruits and if they are harvested before fully ripe their quality is compromised. This is because sugar content, aroma and color compounds increase only before harvest for non-climacteric fruits. The current practice for determining berry ripeness includes measurements of berry samples for total soluble solids (TSS) and pH, but this procedure is time consuming and laborious. On the other hand, with the development of unmanned aerial vehicles (UAV) and modern ultralight cameras the grower can now obtain data rapidly and also spatial information for crop's physiological status at farm scale. Berry samples were collected from grapevines (cv. Malagousia) and their reflectance spectra were used to estimate TSS and pH by Multiple Linear Regression (MLR) and Support Vector Machine (SVM). The highest classification accuracy was achieved using the SVM model. Moreover, berries taken by grapevines with low Carotenoid Reflectance Index 2 (CRI2) had higher TSS, pH and terpenes, and gave wine with higher alcohol by volume. The importance for constructing a model for predicting TSS in berries is obvious, because this can aid in the prediction of wine quality. The current work is a preliminary compilation of methodologies for making a monitoring tool of berry ripeness, using statistical techniques, remote sensing and crop physiological data.