Leaf area estimation of reconstructed maize plants using a time-of-flight camera

Publication Type

Contribution to conference

Authors

Vázquez-Arellano , Reiser , Paraforos , Garrido - lzard, Griepentrog

Year of publication

2018

Published in

Bornimer Agrartechnische Berichte Heft 101

Editor

Leibniz - Institut für Agrartechnik und Bioökonomie e.V. (ATB)

Pubisher

Leibniz - Institut für Agrartechnik und Bioökonomie e .V , Potsdamm

Band/Volume

101/

ISBN / ISSN / eISSN

0947-7314

Page (from - to)

110-119

Conference name

6th International Conference on Machine Control and Guidance

Conference location

Berlin

Conference date

1-2.10.2018

The leaf area is an important plant parameter for plant status and crop yield. In this experiment, a low-cost time-of-flight camera, the Kinect v2, was mounted on a robotic platform to acquire 3-D data of maize plants in a greenhouse. The robotic platform drove through the maize rows and acquired 3-D images that were later registered and stitched. Three different maize row reconstruction approaches were compared: reconstruct a crop row by merging point clouds generated from both sides of the row in both directions, merging point clouds scanned just from one side and merging point clouds scanned from opposite directions of the row. The resulted point cloud was sub-sampled and rasterized, the normals were computed and reoriented with a Fast Marching algorithm. The Poisson surface reconstruction was applied to the point cloud and new vertices and faces generated by the algorithm were removed. The results showed that the approach of aligning and merging four point clouds per row and two point clouds scanned from the same side generated very similar average mean absolute percentage error of 8.8% and 7.8%, respectively. The worst error resulted from the two point clouds scanned from both sides in opposite direction with 32.3%.