In precision agriculture, the analysis of UAV-based multispectral imagery enables spatial differentiation of crop condition, supporting targeted management decisions. This study compares the performance of two unsupervised segmentation algorithms (K-means and Gaussian Mixture Models) in analyzing RGB images of winter wheat, supported by NDVI-based interpretation. Segmentation was performed on RGB orthomosaics acquired at two phenological stages, followed by NDVI analysis to assign physiological meaning to each segment. The average NDVI per cluster was used to reconstruct NDVI maps and objectively assess vegetation condition within segments. In the early growth stage, segmentation primarily reflected spectral variability in the soil background due to low biomass and weak plant–soil contrast. NDVI analysis revealed that seemingly regular clusters corresponded to bare inter-row soil rather than emerging plants – highlighting the limited diagnostic value of RGB segmentation alone at this stage. In the later growth stage, both algorithms accurately delineated field plots and intra-field variability. Using five clusters, the analysis identified zones ranging from dense, healthy vegetation to bare soil. These results demonstrate that combining RGB-based unsupervised segmentation with NDVI analysis is an effective tool for mapping spatial heterogeneity in mature crops, while offering limited standalone value in early growth stages without additional spectral verification.