The influence of canopy structure on tropical tree growth has been scantly studied because of the difficulties making field measurements in these dense multi-layered ecosystems. The recent advent of unmanned aerial vehicles (UAVs), has made it easier to collect canopy data, so offering a way to gain a better understanding of forest productivity and thereby improve forest management. In this study, we assessed tree growth prediction using UAV-derived crown measurements as an alternative for field data. Four experimental 9 ha plots were sampled in two forest sites, Yoko in the Democratic Republic of the Congo and Loundoungou in the Republic of Congo. Field inventories were made between 2015 and 2020. For each tree, we computed the diameter increment (DBHI) using censuses and diameter-based competition indices (diameter-based CIs) using the first census. High-resolution orthoimages and digital surface models were acquired with UAVs in 2016 and 2018 in the two sites. They gave estimates of crown characteristics (size, relative elevation, shape) and crown-based competition indices (crown-based CIs). Co-recorded UAV and field measurements were obtained for 1558 trees. The diameter increment of these trees was then modelled using supervised component generalized linear regression, and 20 % of trees were kept for cross-validation. Combined field and UAV data predicted tree DBHI twice better than either taken separately. Diameter at breast height (DBH) and crown area (CA) were found to be complementary predictors. Crown-based CIs significantly improved predictions of models already containing DBH and CA. Adding diameter-based CIs to models containing DBH, CA, and crown-based CIs only marginally improved growth predictions, showing that tree competition can be well-described with UAV data. The model calibrated at one site predicted the growth at the other site well, suggesting that a general model could be devised for multiple sites. Growth variance was better explained in th