Iterative individual plant clustering in maize with assembled 2D LiDAR data

A two dimensional (2D) laser scanner was mounted at the front part of a small 4-wheel autonomous robot with differential steering, at an angle of 30 ° pointing downwards. The machine was able to drive between maize rows and collect concurrent time-stamped data. A robotic total station tracked the position of a prism mounted on the vehicle. The total station and laser scanner data were fused to generate a three dimensional (3D) point cloud. This 3D representation was used to detect individual plant positions, which are of particular interest for applications such as phenotyping, individual plant treatment and precision weeding. Two different methodologies were applied to the 3D point cloud to estimate the position of the individual plants. The first methodology used the Euclidian Clustering on the entire point cloud. The second methodology utilised the position of an initial plant and the fixed plant spacing to search iteratively for the best clusters. The two algorithms were applied at three different plant growth stages. For the first method, results indicated a detection rate up to 73.7% with a root mean square error of 3.6 cm. The second method was able to detect all plants (100% detection rate) with an accuracy of 2.7-3.0 cm, taking the plant spacing of 13 cm into account.