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Following the increased economic importance of the sugarcane crop, a growing interest in monitoring growth and predicting yield of sugarcane has emerged. The time-consuming and pricey nature of collecting ground-based measurements and limitations in level of detail of satellite imagery make Unmanned Aerial Vehicles (UAVs) a potential interesting alternative remote sensing platform for the retrieval of biophysical crop parameters. UAVs can provide high spatial resolution imagery for a relatively low cost while having flexible deployment capabilities. The main objective of this research project was to evaluate the potential of RGB/NIR (Red-Blue-Green/Near-infrared) imagery acquired by UAVs as an alternative for ground observations. More specifically, four variables of interest – canopy height, leaf area index (LAI), stem density and biomass yield – were estimated by analysing UAV imagery in conjunction with ground truth data. Canopy height was estimated from a Digital Surface Model (DSM) generated from overlapping images taken by the UAV. The analysis showed that DSM derived canopy height and observed canopy height were highly correlated (R2 = 0.97). Furthermore, two decision tree-based regression techniques were calibrated and compared for their ability to predict LAI, stem density and biomass yield from the UAV imagery: random forest (RF) regression and boosted regression trees (BRT). The models were trained with a random subset representing 70% of the ground truth data, and then predictions were made on the remaining 30%. The predictive performance of various models was compared by calculating the Root Mean Squared Error (RMSE) and the R2 between the predicted and measured response variables. In general, the data analysis showed that despite imperfections in the data, these variables of interest can be estimated from the UAV images with a satisfactory accuracy. The most suitable model for LAI resulted in an RMSE of 0.72 (-) and R2 of 0.76, for stem density an RMSE of 1.43 stems/m and R2 of 0.92 and for biomass yield an RMSE of 7.35 ton/ha and R2 of 0.84.