Nitrogen content is essential for grass growth, grassland management, and forage productivity. In general, the nitrogen amount is indirectly estimated using manual techniques for sample acquisition and laboratory analysis, which are a costly endeavour, mainly in large agricultural areas. In this context, remote sensing technologies allow monitoring important parameters for agriculture, fast, non-destructively, and on a large scale, using aerial images obtained by Unmanned Aerial Vehicles (UAV) and analysed through spectral indices and structural variables of the vegetation. However, further studies are needed that use more affordable sensor systems that can be used in large areas, such as in Brazil. This work asses the feasibility of employing GoPro wide-angle RGB camera onboard a UAV to estimate the nitrogen content of an experimental grassland area. Different data scenarios were evaluated, incorporating combinations of vegetation indices (VIs) and three-dimensional (3D) metrics derived from the Canopy Height Model (CHM): all available metrics (ALL), a subset of three VIs combined with four 3D metrics (VI3 + CHM4), and 3D metrics only. To estimate nitrogen content, the Random Forest (RF) machine learning algorithm was applied. The most accurate model, yielding the lowest error, resulted from integrating data from two acquisition dates, achieving a coefficient of determination (R²) of 0.83 for the model, a Pearson Correlation Coefficient (PCC) of 0.82 in the validation trials, and a Root Mean Square Error expressed as a percentage (RMSE%) of 19.62%. These findings highlight the potential of UAV-mounted RGB sensors as an effective tool for estimating pasture parameters.