3-D reconstruction of maize plants using a time-of-flight camera

• Published in: Computers and electronics in agriculture 2018-02, Vol.145, p.235-247
• Main Authors: Vázquez-Arellano, Manuel, Reiser, David, Paraforos, Dimitris S., Garrido-Izard, Miguel, Burce, Marlowe Edgar C., Griepentrog, Hans W.
• Format: Article
• Language: English
• Subjects: 3-D sensors; Agricultural practices; Agricultural robotics; Agriculture; Architecture; Automation; Cluster analysis; Clustering; Corn; Ground truth; Image resolution; Image segmentation; Iterative algorithms; Kinect v2; Lidar; Methodology; Points; Precision farming; Reconstruction; Soils; Standard deviation; Stitching; Studies; Three dimensional models; Time-of-flight; Vector quantization
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2018.01.002

[Display omitted] •The ICP algorithm was utilised to create point clouds of maize crop rows.•A qualitative analysis was performed of the reconstructed maize plant point clouds.•A k-means clustering at the base of the plant point cloud was used for validation.•The developed methodology managed to generate highly dense 3-D point clouds in a cost-effective manner. Point cloud rigid registration and stitching for plants with complex architecture is a challenging task, however, it is an important process to take advantage of the full potential of 3-D cameras for plant phenotyping and agricultural automation for characterizing production environments in agriculture. A methodology for three-dimensional (3-D) reconstruction of maize crop rows was proposed in this research, using high resolution 3-D images that were mapped into the colour images using state-of-the art software. The point cloud registration methodology was based on the Iterative Closest Point (ICP) algorithm. The incoming point cloud was previously filtered using the Random Sample Consensus (RANSAC) algorithm, by reducing the number of soil points until a threshold value was reached. This threshold value was calculated based on the approximate number of plant points in a single 3-D image. After registration and stitching of the crop rows, a plant/soil segmentation process was done relying again on the RANSAC algorithm. A quantitative comparison showed that the number of points obtained with a time-of-flight (TOF) camera, compared with the ones from two light detection and ranging (LIDARs) from a previous research, was roughly 23 times larger. Finally, the reconstruction was validated by comparing the seedling positions as ground truth and the point cloud clusters, obtained using the k-means clustering, that represent the plant stem positions. The resulted maize positions from the proposed methodology closely agreed with the ground truth with an average mean and standard deviation of 3.4 cm and ±1.3 cm, respectively.