The use of Unmanned Aerial Vehicles (UAVs) equipped with multispectral and hyperspectral sensors has become fundamental in precision agriculture applications. The images acquired by these sensors are commonly represented in Digital Numbers (DNs), which are influenced by various external conditions and therefore do not directly reflect the true surface reflectance. In order to make this data usable, radiometric calibration is necessary to preserve the spectral properties of the scene. This study evaluates the radiometric accuracy of images obtained with the DJI Mavic 3M UAV by comparing three sets of reflectance data: (i) the orthomosaics generated from automatic calibration using the DJI Mavic 3M UAV's sunlight sensor; (ii) the orthomosaic calibrated using the empirical line method (ELM); and (iii) the reflectance values obtained in the field with the ASD FieldSpec spectroradiometer. Ten radiometric targets with different physical properties and coloring were distributed over the study area to determine the linear regression coefficients, based on the selection of the most suitable targets for vegetation prediction. The geometric processing of the multispectral image block was carried out using Agisoft Metashape software, while the radiometric adjustment via ELM was conducted using Excel and QGIS. The results show that the ELM produced reflectance estimates for vegetation that were highly consistent with field measurements (MAE ≤ 0.009), while the automatic method overestimated the values in all bands. The findings highlight the limitations of automatic calibration and reinforce the effectiveness of ELM for applications that require high accuracy in estimating the reflectance of the target of interest.