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Clustering of Laser Scanner Perception Points of Maize Plants

Published online by Cambridge University Press:  01 June 2017

D. Reiser ,
M. Vázquez-Arellano ,
M. Garrido Izard ,
D. S. Paraforos ,
G. Sharipov  and
H. W. Griepentrog
D. Reiser*
Affiliation:
University of Hohenheim, Institute of Agricultural Engineering, Garbenstr. 9, D-70599, Stuttgart, Germany
M. Vázquez-Arellano
Affiliation:
University of Hohenheim, Institute of Agricultural Engineering, Garbenstr. 9, D-70599, Stuttgart, Germany
M. Garrido Izard
Affiliation:
Laboratorio de Propiedades Físicas (LPF)-TAGRALIA, Technical University of Madrid, Madrid 28040, Spain
D. S. Paraforos
Affiliation:
University of Hohenheim, Institute of Agricultural Engineering, Garbenstr. 9, D-70599, Stuttgart, Germany
G. Sharipov
Affiliation:
University of Hohenheim, Institute of Agricultural Engineering, Garbenstr. 9, D-70599, Stuttgart, Germany
H. W. Griepentrog
Affiliation:
University of Hohenheim, Institute of Agricultural Engineering, Garbenstr. 9, D-70599, Stuttgart, Germany
*
† E-mail: dreiser@uni-hohenheim.de
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Abstract

The goal of this work was to cluster maize plants perception points under six different growth stages in noisy 3D point clouds with known positions. The 3D point clouds were assembled with a 2D laser scanner mounted at the front of a mobile robot, fusing the data with the precise robot position, gained by a total station and an Inertial Measurement Unit. For clustering the single plants in the resulting point cloud, a graph-cut based algorithm was used. The algorithm results were compared with the corresponding measured values of plant height and stem position. An accuracy for the estimated height of 1.55 cm and the stem position of 2.05 cm was achieved.

Keywords

LiDARsingle plant detectiongraph-cutstem detectionphenotyping

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