Remote sensing with large-scale satellite images for precision studies in grasslands has spatial and spectral resolution limitations. Against this, using spectral signs and vegetation indices obtained with microsensors transported by unmanned aerial vehicles (UAV) constitutes a more accurate alternative for biomass estimation. In the fieldwork, images were acquired with microsensors, and fixed transects of 100 m were used where vegetation samples were collected. The photographs acquired with the UAV were processed in Pix 4D, Arc Gis, and algorithms elaborated in R programming language. The biomass estimation was carried out with Multiple Linear Regression, Vector Support Machine, and Random (Forest Random) models. The Random model showed a Kappa coefficient of 0.94 in the training set and 0.901 in the test set (R2 = 0.482). The Random Forest model predicted 3 g/pixel of MV for Puna grass...