Durum wheat (Triticum turgidum ssp. durum) represents a strategic crop for the Mediterranean basin and global semiarid regions, being the raw material for pasta and a key component of sustainable cereal production. Improving early vigor and canopy development is essential to enhance resource-use efficiency and yield stability under variable agronomic conditions. For these reasons, we report the application of a series of RGB-derived vegetation indices (VIs) from Unmanned Aerial Vehicle (UAVs) to evaluate their effectiveness in capturing canopy variation in the early growth stages in a large collection of durum wheat varieties and on their validation under different agronomic managements. Digital RGB images from seedling emergence to grain filling were taken in two field experiments, and RGB-based indices were calculated over four consecutive growing seasons. In the first experiment, 521 durum wheat varieties were evaluated, showing highly significant genotypic differences for all VIs (p < 0.001) and explaining up to 72% of the phenotypic variance at the end of tillering. In addition, TGI explained more variation than CSI when recorded at the end of the tillering stage. In the second experiment, two contrasting genotypes managed under two sowing rates and six nitrogen (N) treatments displayed a strong discriminating capacity of NGRDI and TGI for genotype and sowing density (eta(2) = 0.50). These results highlight the potential use of RGB-derived VIs for high-throughput phenotypic selection of soil coverage ability in durum wheat, even under different agronomic conditions.