Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-30T20:13:46.061Z Has data issue: false hasContentIssue false
  • English
  • Français

Sensing in the visible spectrum and beyond for terrain estimation in precision agriculture

Published online by Cambridge University Press:  01 June 2017

A. Milella ,
M. Nielsen  and
G. Reina
A. Milella*
Affiliation:
Institute of Intelligent Systems for Automation, National Research Council, Bari, Italy
M. Nielsen
Affiliation:
Danish Technological Institute, Odense, Denmark
G. Reina
Affiliation:
University of Salento, Lecce, Italy
*
†E-mail: milella@ba.issia.cnr.it
Get access

Abstract

A multi-sensor approach for terrain estimation is proposed using a combination of complementary optical sensors that cover the visible (VIS), near infrared (NIR) and infrared (IR) spectrum. The sensor suite includes a stereovision sensor, a VIS-NIR camera and a thermal camera, and it is intended to be mounted on board an agricultural vehicle, pointing downward to scan the portion of the terrain ahead. A method to integrate the different sensor data and create a multi-modal dense 3D terrain map is presented. The stereovision input is used to generate 3D point clouds that incorporate RGB-D information, whereas the VIS-NIR camera and the thermal sensor are employed to extract respectively spectral signatures and temperature information, to characterize the nature of the observed surfaces. Experimental tests carried out by an off-road vehicle are presented, showing the feasibility of the proposed approach.

Keywords

Autonomous intelligent vehiclesagricultural roboticsterrain estimationoptical sensorssensing in the visible and beyond
Type
Soil Sensing and Variability
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. 423 - 429
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ball, D, Ross, P, English, A, Patten, T, Upcroft, B, Fitch, R, Sukkarieh, S, Wyeth, G and Corke, P 2015. Robotics for Sustainable Broad-Acre Agriculture Field and Service Robotics. Springer Tracts in Advanced Robotics 105, 439–453.Google Scholar
Bouguet, JY. 2008. Camera calibration toolbox for Matlab (2008). Retrieved on 31 January 2017 from https://www.vision.caltech.edu/bouguetj/calib_doc.Google Scholar
Geiger, A, Ziegler, J and Stiller, C 2011. StereoScan: Dense 3D Reconstruction in Real-time. In: Intelligent Vehicles Symposium (IV). 2011 IEEE (pp. 963-968).Google Scholar
Hirschmuller, H. 2005. Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information. In Int Conf on Computer Vision and Pattern Recognition. 2005, CVPR 2005, vol. 2, pp. 807–814.Google Scholar
Kragh, M, Jorgensen, R and Pedersen, H 2015. Object detection and terrain classification in agricultural fields using 3D lidar data. In Computer Vision Systems: 10th International Conference, ICVS 2015, Springer International Publishing.Google Scholar
Milella, A, Reina, G and J. Underwood, J 2015. A self-learning framework for statistical ground classification using radar and monocular vision. Journal of Field Robotics 32 (1), 20–41.Google Scholar
Mulder, VL, de Bruin, S, Schaepman, ME and Mayr, TR 2011. The use of remote sensing in soil and terrain mapping - A review. Geoderma 162, 1–19.Google Scholar
Nieto, JI, Monteiro, ST and Viejo, D 2010. 3D geological modelling using laser and hyperspectral data. Published in: Geoscience and Remote Sensing Symposium (IGARSS), IEEE International.Google Scholar
Rangel, J, Soldan, S and Kroll, A 2014. 3D Thermal Imaging: Fusion of Thermography and Depth Cameras. In: 12th International Conference on Quantitative Infrared Thermography, Bordeaux, France.Google Scholar
Reina, G and Milella, A 2012. Towards autonomous agriculture: Automatic ground detection using trinocular stereovision. Sensors 12 (9), 12405–12423.Google Scholar
Reina, G, Milella, A, Rouveure, R, Nielsen, M, Worst, R and Blas, MR 2016. Ambient awareness for agricultural robotic vehicles. Biosystems Engineering 146, 114–132.Google Scholar
Ross, P, English, A, Ball, D, Upcroft, B and Corke, P 2015. Online novelty-based visual obstacle detection for field robotics. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3935-3940.Google Scholar
Stettler, M, Keller, T, Weisskopf, P, Lamand, M, Lassen, P and Schjnning, P 2014. Terranimo: a web-based tool for evaluating soil compaction. Landtech 69 (3), 132–137.Google Scholar