Precision agriculture has seen significant advancements with the integration of remote sensing technologies. However, challenges such as real-time data availability, standardization, and computing limitations in rural settings persist. This study aimed to develop a standardized method for generating spatial variability maps for crop management in vineyards using UAV imagery. Using IDW (Inverse Distance Weight),a geostatistical interpolation method, nadir images with geotagged locations were processed to extract spectral information and EXIF metadata. The results demonstrated that interpolation methods are effective compared to traditional photogrammetry-based methods, with the approach being more than 90% faster, highlighting their potential in real-time applications. Notably, IDW's correlation with Sentinel 2 imagery reached values as high as r = 0.8, comparable to orthomosaics. This method offers a faster, less resource-intensive alternative to existing techniques for crop mapping, addressing the current challenges in precision agriculture. Its practical implications suggest that farmers and agricultural professionals can achieve accurate spatial variability assessments without the need for high-end equipment or extensive computing power, making it a cost-effective and efficient solution for modern agriculture.