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Remote Sens. 2015, 7(12), 17077-17096; doi:10.3390/rs71215870

3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds

1
Laboratorio de Propiedades Físicas (LPF)-TAGRALIA, Technical University of Madrid, Madrid 28040, Spain
2
Institute of Agricultural Engineering, University of Hohenheim, Garbenstr. 9, Stuttgart D-70599, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 14 October 2015 / Revised: 19 November 2015 / Accepted: 1 December 2015 / Published: 17 December 2015
View Full-Text   |   Download PDF [10866 KB, uploaded 17 December 2015]   |  

Abstract

3D crop reconstruction with a high temporal resolution and by the use of non-destructive measuring technologies can support the automation of plant phenotyping processes. Thereby, the availability of such 3D data can give valuable information about the plant development and the interaction of the plant genotype with the environment. This article presents a new methodology for georeferenced 3D reconstruction of maize plant structure. For this purpose a total station, an IMU, and several 2D LiDARs with different orientations were mounted on an autonomous vehicle. By the multistep methodology presented, based on the application of the ICP algorithm for point cloud fusion, it was possible to perform the georeferenced point clouds overlapping. The overlapping point cloud algorithm showed that the aerial points (corresponding mainly to plant parts) were reduced to 1.5%–9% of the total registered data. The remaining were redundant or ground points. Through the inclusion of different LiDAR point of views of the scene, a more realistic representation of the surrounding is obtained by the incorporation of new useful information but also of noise. The use of georeferenced 3D maize plant reconstruction at different growth stages, combined with the total station accuracy could be highly useful when performing precision agriculture at the crop plant level. View Full-Text
Keywords: LiDAR; total station; crop characterization; scan orientation; point cloud overlapping; ICP; registration; 3D; maize plants; plant phenotyping LiDAR; total station; crop characterization; scan orientation; point cloud overlapping; ICP; registration; 3D; maize plants; plant phenotyping
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Garrido, M.; Paraforos, D.S.; Reiser, D.; Vázquez Arellano, M.; Griepentrog, H.W.; Valero, C. 3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds. Remote Sens. 2015, 7, 17077-17096.

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