In this thesis, we develop algorithms to estimate crop heights as well as to detect plots infarms. Plant height estimation is needed in precision agriculture to monitor plant health andgrowth cycles. We use a 3D LiDAR mounted on an Unmanned Aerial Vehicle (UAV) anduse the LiDAR data for height and plot estimation. We present a general methodology forextracting plant heights from 3D LiDAR with two specific variants for the two environments:row-crops and pasture. The main algorithm is based on ground plane estimation from 3DLiDAR scans, which is then used to determine the height of plants in the scans. For rowcrops, the plot detection uses a K-means clustering algorithm to find the bounding boxes ofthese clusters, and a voting scheme to determine the best-fit width, height, and orientationof the clusters/plots. This best-fit box is then used to create a grid over the LiDAR dataand the plots are extracted. For pasture, relative heights are estimated using data collectedweekly. Both algorithms we evaluated using data collected from actual farms and pasture.The accuracy in plot height estimation was +/- 5.36 % and that for growth estimates was+/- 7.91 %.