Efficient plant phenotyping methods are necessary to accelerate the development of high yield biofuel crops. Manual measurement of plant phenotypes, such as height is inefficient, labor intensive and error prone. We present a robust, LiDAR based approach to estimate the height of biomass sorghum plants. A vertically oriented laser rangefinder onboard an agricultural robot captures LiDAR scans of the environment as the robot traverses between crop rows. These LiDAR scans are used to generate height contours for a single row of plants corresponding to a given genetic strain. We apply ground segmentation, iterative peak detection and peak filtering to estimate the average height of each row. Our LiDAR based approach is capable of estimating height at all stages of the growing period, from emergence e.g. 10 cm through canopy closure e.g. 4 m. Our algorithm has been extensively validated by several ground truthing campaigns on biomass sorghum. These measurements encompass typical methods employed by breeders as well as higher accuracy methods of measurement. We are able to achieve an absolute height estimation error of 8.46% ground truthed via ?by-eye? method over 2842 plots, an absolute height estimation error of 5.65% ground truthed at high granularity by agronomists over 12 plots, and an absolute height estimation error of 7.2% when ground truthed by multiple agronomists over 12 plots.