Functional-structural plant modelling approaches (FSPM) open the way for exploring the relationships between the 3D structure and the physiological functioning of plants in relation to environmental conditions. FSPMs can be particularly interesting when dealing with perennial crops like oil palm, for which research on innovative management practices requires long and expensive agronomic trials. The present study is part of the PalmStudio project, which aims at developing a FSPM for oil palm capable of conducting virtual experiments to test the relevance of innovative management practiceds and/or design ideotypes. We propose a methodological approach which integrates architectural responses to planting density in an existing oil palm FSPM (Perez et al. 2018a b). Combining standard field phenotyping with Lidar-based derived measurements, we manage to evaluate the phenotypic plasticity of the main parameters required for the calibration of the 3D plant model. LiDAR scans were processed using the PlantScan3D software (Boudon et al. 2014) to derive phenotypic traits of leaf geometry that were compared to labour-intensive measurements. Density-based allometries of leaf geometry and biomass are then derived from the observed variations in phenotypic traits and integrate into the FSPM. Our results illustrate the accuracy and the efficiency of Lidar-based phenotyping of leaf geometrical traits. In average, we find less than 3% of difference in leaf dimensions (i.e. rachis length) in comparison with traditional hand-made field measurements. The fast and efficient measurements of usually labour-intensive traits such as leaf curvature allowed estimating the plasticity of leaf geometry in response to density. We find that the main traits affected by density were leaf dimensions (up to 15% and 25% of increase in rachis length and petiole length respectively) and curvature (15% of increase in leaf erectness-related parameter), whereas other structural traits like the number of lea