Determination of stem position and height of reconstructed maize plants using a time-of-flight camera

Three dimensional (3-D) reconstruction of maize plant morphology by proximal sensing in agriculture brings high definition data that can be used for a number of applications related with precision agriculture and agricultural robotics. However, 3-D reconstruction without methodologies for extracting useful information is a senseless strategy. In this research, a methodology for stem position estimation is presented relying on the merging of four point clouds, using the Iterative Closes Point algorithm, that were generated from different 3-D perspective views. The proposed methodology is based on bivariate point density histograms for detecting the regional maxima and a radius filter based on the closest Euclidean distance. Then, single plant segmentation was performed by projecting a spatial cylindrical boundary around the estimated stem positions on a merged plant and soil point cloud. After performing a local Random Sample Consensus, the segmented plant point cloud was clustered using the Density-based spatial clustering of applications with noise algorithm. Additionally, a height profile was generated by rasterizing the plant and soil point clouds, separately, with different cell widths. The rasterized soil point cloud was meshed, and the rasterized plant points to soil mesh distance was calculated. The resulting plant stem positions were estimated with an average mean error and standard deviation of 24 mm and 14 mm, respectively. Equivalently, the average mean error and standard deviation of the individual plant height estimation was 30 mm and 35 mm, respectively. Finally, the overall plant height profile mean error average was 8.7 mm. Thus it is possible to determine the stem position and plant height of reconstructed maize plants using a low-cost time-of-flight camera.