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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: [email protected]
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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
Type
Crop Sensors and Sensing
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 204 - 209
Copyright
© The Animal Consortium 2017 

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References

Bac, CW, Hemming, J and Henten, EJV 2014. Stem localization of sweet-pepper plants using the support wire as a visual cue. Computers and Electronics in Agriculture 105, 111–120.CrossRefGoogle Scholar
Bechar, A and Vigneault, C 2016. Agricultural robots for field operations: Concepts and components. Biosystems Engineering 149, 94–111.CrossRefGoogle Scholar
Bentley, JL 1975. Multidimensional Binary Search Trees Used for Associative Searching. Communications of the ACM 18 (9), 509–517.Google Scholar
Boykov, Y and Funka-Lea, G 2006. Graph Cuts and Efficient N-D Image Segmentation. International Journal of Computer Vision 70 (2), 109–131.CrossRefGoogle Scholar
Escolà, A, Martinez-Casanovas, JA, Rufat, J, Arnó, J, Arbonés, A, Sebé, F, et al. 2017. Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds. Precision Agriculture. doi: 10.1007/s11119-016-9474-5.CrossRefGoogle Scholar
Fischler, MA and Bolles, RC 1981. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24, 381–395.Google Scholar
Garrido, M, Paraforos, D, Reiser, D, Vázquez Arellano, M, Griepentrog, H and Valero, C 2015. 3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds. Remote Sensing 7 (12), 17077–17096.Google Scholar
Garrido, M, Perez-Ruiz, M, Valero, C, Gliever, CJ, Hanson, BD and Slaughter, DC 2014. Active optical sensors for tree stem detection and classification in nurseries. Sensors 14 (6), 10783–10803.CrossRefGoogle ScholarPubMed
Golovinskiy, A and Funkhouser, T 2009. Min-Cut Based Segmentation of Point Clouds. IEEE Workshop on Search in 3D and Video (S3DV) at ICCV.Google Scholar
Griepentrog, HW, Norremark, M, Nielsen, H and Blackmore, BS 2005. Seed mapping of sugar beet. Precision Agriculture 6 (2), 157–165.5.CrossRefGoogle Scholar
Reiser, D, Izard, MG, Arellano, MV, Griepentrog, HW and Paraforos, DS 2016. Crop Row Detection in Maize for Developing Navigation Algorithms under Changing Plant Growth Stages. In Advances in Intelligent Systems and Computing 417, 371–382. Lisbon: Springer, Portugal.Google Scholar
Reitberger, J, Krzystek, P and Stilla, U 2007. Combined tree segmentation and stem detection using full waveform lidar data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 3, 332–337.Google Scholar
Ritchie, S, Hanway, J and Benson, G 1993. How A Corn Plant Develops. Special Report No. 48 (revised); Iowa State University of Science and Technology Cooperative Extension Service: Ames, IA, USA.Google Scholar
Rusu, RB and Cousins, S 2011. 3D is here: point cloud library. IEEE International Conference on Robotics and Automation 1–4.CrossRefGoogle Scholar
Sick AG Waldkirch 2016. Operating Instructions LMS1xx. https://mysick.com/saqqara/im0031331.pdf. (Retrieved 11 January 2017).Google Scholar
Vázquez-Arellano, M, Griepentrog, HW, Reiser, D and Paraforos, DS 2016. 3-D Imaging Systems for Agricultural Applications – A Review. Sensors 16, 1–24.CrossRefGoogle ScholarPubMed
Weiss, U and Biber, P 2011. Plant detection and mapping for agricultural robots using a 3D LIDAR sensor. Robotics and Autonomous Systems 59 (5), 265–273.Google Scholar
Zhang, Y, Teng, P, Shimizu, Y, Hosoi, F and Omasa, K 2016. Estimating 3D Leaf and Stem Shape of Nursery Paprika Plants by a Novel Multi-Camera. Sensors 16 (874), 1–18.Google Scholar