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3D monitoring of woody crops using an unmanned ground vehicle

Published online by Cambridge University Press:  01 June 2017

A. Ribeiro ,
J. M. Bengochea-Guevara ,
J. Conesa-Muñoz ,
N. Nuñez ,
K. Cantuña  and
D. Andújar
A. Ribeiro*
Affiliation:
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
J. M. Bengochea-Guevara
Affiliation:
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
J. Conesa-Muñoz
Affiliation:
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
N. Nuñez
Affiliation:
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
K. Cantuña
Affiliation:
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
D. Andújar
Affiliation:
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
*
†E-mail: angela.ribeiro@csic.es
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Abstract

This paper presents an inspection system integrated into an on-ground autonomous platform with range of approximately 80 km. The vehicle is prepared to autonomously cover a field following a predefined route plan. Two types of cameras were integrated in the platform. An RGB-D sensor and a reflex camera were placed in a fixture and connected to a high-performance computer. The heterogeneous information acquired from the RGB-D was later integrated to automatically generate 3D maps of the crops by using custom software developed in the authors’ previous work. The inspection system performance was tested in actual vineyards by conducting several samplings in 2016. Results show that the proposed technology is viable and can provide complementary information to other inspection alternatives.

Keywords

3D woody crop reconstructioncanopy volume estimationRGB-D sensor
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. 210 - 215
Copyright
© The Animal Consortium 2017 

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References

Alphashape3d package 2016. https://cran.r-project.org/web/packages/alphashape3d/index.html (retrieved 09/12/16).Google Scholar
Bechar, A and Vigneault, C 2016. Agricultural robots for field operations: Concepts and components. Biosystems Engineering 149, 94–111.Google Scholar
Bengochea-Guevara, JM, Conesa-Muñoz, J, Andújar, D and Ribeiro, A 2016. Merge Fuzzy Visual Servoing and GPS-Based Planning to Obtain a Proper Navigation Behavior for a Small Crop-Inspection Robot. Sensors 16 (3), 276–298.Google Scholar
Burgos-Artizzu, XP, Ribeiro, A, Tellaeche, A, Pajares, G and Fernández-Quintanilla, C 2010. Analysis of natural images processing for the extraction of agricultural elements. Image and Vision Computing 28 (1), 138–149.CrossRefGoogle Scholar
Conesa-Muñoz, J, Bengochea-Guevara, JM, Andújar, D and Ribeiro, A 2016. Route planning for agricultural tasks: a general approach for fleets of autonomous vehicles in site-specific herbicide applications. Computers and Electronics in Agriculture 127, 204–220.Google Scholar
Chen, Y and Medioni, G 1992. Object modelling by registration of multiple range images. Image and Vision Computing 10 (3), 145–155.CrossRefGoogle Scholar
Chen, J, Bautembach, D and Izadi, S 2013. Scalable real-time volumetric surface reconstruction. ACM Transactions on Graphics 32 (4), 113–128.Google Scholar
Edelsbrunner, H and Mücke, EP 1994. Three-dimensional alpha shapes. ACM Transactions on Graphics 13 (1), 43–72.Google Scholar
Fraichard, T and Garnier, P 2001. Fuzzy control to drive car-like vehicles. Robotics and Autonomous Systems 34, 1–22.Google Scholar
Kodagoda, KRS, Wijesoma, WS and Teoh, EK 2002. Fuzzy speed and steering control of an AGV. IEEE Transactions on Control Systems Technology 10, 112–120.Google Scholar
Naranjo, JE, Sotelo, M, Gonzalez, C, Garcia, R and Sotelo, MA 2007. Using fuzzy logic in automated vehicle control. IEEE Intelligent Systems 22, 36–45.Google Scholar
Newcombe, RA, Izadi, S, Hilliges, O, Molyneaux, D, Kim, D, Davison, AJ et al 2011. KinectFusion: Real-time dense surface mapping and tracking. In Proceedings 10th IEEE international symposium on Mixed and augmented reality, pp.127-136.Google Scholar
Niessner, M, Zollhöfer, M, Izadi, S and Stamminger, M 2013. Real-time 3d reconstruction at scale using voxel hashing. ACM Transactions on Graphics 32 (6), 169–179.Google Scholar
Steinbrucker, F, Kerl, C and Cremers, D 2013. Large-scale multi-resolution surface reconstruction from RGB-D sequences. In Proceedings of the IEEE International Conference on Computer Vision, pp. 3264–3271.Google Scholar
Sugeno, M 1999. On stability of fuzzy systems expressed by fuzzy rules with singleton consequents. IEEE Transactions on Fuzzy Systems 7, 201–224.CrossRefGoogle Scholar
Whelan, T, Kaess, M, Fallon, M, Johannsson, H, Leonard, J and McDonald, J 2012. Kintinuous: Spatially extended kinectfusion. Computer Science and Artificial Intelligence Laboratory Technical Report MIT-CSAIL-TR-2012-020. Massachusetts Institute of Technology, Cambridge, Massachusetts, EEUU 8 pp.Google Scholar
Zadeh, LA 1965. Fuzzy sets. Information and Control 8, 338–353.Google Scholar